{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transpiler Passes and Pass Manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A central component of Qiskit Terra is the transpiler, which is designed for modularity and extensibility. The goal is to be able to easily write new circuit transformations (known as transpiler **passes**), and combine them with other existing passes.  Which passes are chained together and in which order has a major effect on the final outcome. This pipeline is determined by a **pass manager**, which schedules the passes and also allows passes to communicate with each other by providing a shared space. In this way, the transpiler opens up the door for research into aggressive optimization of quantum circuits.\n",
    "\n",
    "In this notebook, we look at the built-in passes, how to use the pass manager, and develop a simple custom transpiler pass. In order to do the latter, we first need to introduce the internal representation of quantum circuits in Qiskit, in the form of a Directed Acyclic Graph, or **DAG**. Then, we illustrate a simple swap mapper pass, which transforms an input circuit to be compatible with a limited-connectivity quantum device.\n",
    "\n",
    "***Before you start***: You may need to install the `pydot` library and the `graphviz` library for the DAG plotting routines. If you are using Anaconda Python, you can install both with the `conda` command. If you use your system's native Python interpreter, install `pydot` using the `pip` command, and install `graphviz` using your system's native package manager (e.g. `yum`, `apt`, `dnf`, `brew`, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:56.857930Z",
     "start_time": "2019-12-10T21:47:54.444353Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.compiler import transpile\n",
    "from qiskit.transpiler import PassManager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T09:12:12.822442Z",
     "start_time": "2019-08-21T09:12:12.819902Z"
    }
   },
   "source": [
    "## PassManager object\n",
    "\n",
    "Lets you specify the set of passes you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:57.268332Z",
     "start_time": "2019-12-10T21:47:56.860709Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 200.977x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = QuantumCircuit(3)\n",
    "circ.ccx(0, 1, 2)\n",
    "circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:57.533035Z",
     "start_time": "2019-12-10T21:47:57.270693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 802.977x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.transpiler.passes import Unroller\n",
    "pass_ = Unroller(['u1', 'u2', 'u3', 'cx'])\n",
    "pm = PassManager(pass_)\n",
    "new_circ = pm.run(circ)\n",
    "new_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of Qiskit's transpiler passes are accessible from ``qiskit.transpiler.passes``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:57.539422Z",
     "start_time": "2019-12-10T21:47:57.535048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ApplyLayout',\n",
       " 'BarrierBeforeFinalMeasurements',\n",
       " 'BasicSwap',\n",
       " 'BasisTranslator',\n",
       " 'CSPLayout',\n",
       " 'CXCancellation',\n",
       " 'CXDirection',\n",
       " 'CheckCXDirection',\n",
       " 'CheckMap',\n",
       " 'Collect2qBlocks',\n",
       " 'CommutationAnalysis',\n",
       " 'CommutativeCancellation',\n",
       " 'ConsolidateBlocks',\n",
       " 'CountOps',\n",
       " 'CountOpsLongestPath',\n",
       " 'CrosstalkAdaptiveSchedule',\n",
       " 'DAGFixedPoint',\n",
       " 'DAGLongestPath',\n",
       " 'Decompose',\n",
       " 'DenseLayout',\n",
       " 'Depth',\n",
       " 'EnlargeWithAncilla',\n",
       " 'FixedPoint',\n",
       " 'FullAncillaAllocation',\n",
       " 'HoareOptimizer',\n",
       " 'Layout2qDistance',\n",
       " 'LayoutTransformation',\n",
       " 'LookaheadSwap',\n",
       " 'MergeAdjacentBarriers',\n",
       " 'NoiseAdaptiveLayout',\n",
       " 'NumTensorFactors',\n",
       " 'Optimize1qGates',\n",
       " 'OptimizeSwapBeforeMeasure',\n",
       " 'RemoveDiagonalGatesBeforeMeasure',\n",
       " 'RemoveFinalMeasurements',\n",
       " 'RemoveResetInZeroState',\n",
       " 'ResourceEstimation',\n",
       " 'SabreLayout',\n",
       " 'SabreSwap',\n",
       " 'SetLayout',\n",
       " 'Size',\n",
       " 'StochasticSwap',\n",
       " 'TrivialLayout',\n",
       " 'UnitarySynthesis',\n",
       " 'Unroll3qOrMore',\n",
       " 'UnrollCustomDefinitions',\n",
       " 'Unroller',\n",
       " 'Width']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.transpiler import passes\n",
    "[pass_ for pass_ in dir(passes) if pass_[0].isupper()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different Variants of the Same Pass\n",
    "\n",
    "There can be passes that do the same job, but in different ways. For example, the ``TrivialLayout``, ``DenseLayout`` and ``NoiseAdaptiveLayout`` all choose a layout (binding of virtual qubits to physical qubits), but use different algorithms and objectives. Similarly, the ``BasicSwap``, ``LookaheadSwap`` and ``StochasticSwap`` all insert swaps to make the circuit compatible with the coupling map. The modularity of the transpiler allows plug-and-play replacements for each pass.\n",
    "\n",
    "Below, we show the swapper passes all applied to the same circuit, to transform it to match a linear chain topology. You can see differences in performance, where the ``StochasticSwap`` is clearly the best. However, this can vary depending on the input circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:57.681468Z",
     "start_time": "2019-12-10T21:47:57.541513Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit.transpiler import CouplingMap, Layout\n",
    "from qiskit.transpiler.passes import BasicSwap, LookaheadSwap, StochasticSwap\n",
    "\n",
    "coupling = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6]]\n",
    "\n",
    "circuit = QuantumCircuit(7)\n",
    "circuit.h(3)\n",
    "circuit.cx(0, 6)\n",
    "circuit.cx(6, 0)\n",
    "circuit.cx(0, 1)\n",
    "circuit.cx(3, 1)\n",
    "circuit.cx(3, 0)\n",
    "\n",
    "coupling_map = CouplingMap(couplinglist=coupling)\n",
    "\n",
    "bs = BasicSwap(coupling_map=coupling_map)\n",
    "pass_manager = PassManager(bs)\n",
    "basic_circ = pass_manager.run(circuit)\n",
    "\n",
    "ls = LookaheadSwap(coupling_map=coupling_map)\n",
    "pass_manager = PassManager(ls)\n",
    "lookahead_circ = pass_manager.run(circuit)\n",
    "\n",
    "ss = StochasticSwap(coupling_map=coupling_map)\n",
    "pass_manager = PassManager(ss)\n",
    "stochastic_circ = pass_manager.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:57.902461Z",
     "start_time": "2019-12-10T21:47:57.682997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.977x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:58.238179Z",
     "start_time": "2019-12-10T21:47:57.904473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bbXutodxC5dggIkn9+/dvtr26uloFBQVNl2V1JAc/3aBj+7c03S/eua7ZfSeqr63S1jceabpfV1OpbW88al9BAICY0Vhfqy1rftl0v6G+Rh+v+aU8HufN6k1I7BryvvEJkTmxeiHqqs9o2xuPNd2vPXtK29c9ZWNFbQv1uY5PSI5wJe0Tct2du0S4ErMcOeUlNTVVklRUVKTp06c3bV+yZIlKS0s1duxYY7U4YS6/b3eszvHW0F3GuBmac88MR1/i5DsnRJI6J6XoB0/vaTGBHQCA9vKdEyJZH+Dn/mp7iwnsTlBWKeX/Nfh+XTpLZyvKHXVZmdR8TogkJXbpoQW/PdBiArsTHDghPfr3wPu4JA3oneiIz3hej/7dajcdrKI/LntKmQOdGQLPh8N+1S3Dhw9XTk6OFi1apJdeeknr16/X/Pnz9fzzz0tShxoR8W/Re/9U6TshtPa1m38IkUJr7QsAQDD+IUQKrbWvXXqnSBn9rQ/AgUxIc9bcFqllCJFCa+1rl6G9pX7dAz/XHjmr1bAkXZUePIT06CKN7B9kpyjjsF93S1xcnFauXKmsrCzNnz9fs2fPVmpqqvLy8hQfH6+cnBy7SzTCP4R4J6a31k3LSWHEvzuWV1vdtAAACJV/dyyvtrppOcXNY6T4Tq1fxeCS1LOLNDnTeFkB+XfH8mqrm5YTuFzS7Zdb/7YVRgb2kManmawquLFDpUtSA+9z2+XOC6oXyrE/TkZGhjZu3KiqqioVFxcrPz9fO3bsUGZmppKTz13XV19fr5qaGrnd7qavnTTUdr7aCiFeTg0jrbXo9QrU2hcAgGBaa9HrFai1rxMM7mVd1dD3opaPjegn/duN0kUOmh7SWoter0CtfZ0go780d4rUo5VpF18ZbK2J4rT1OOI7SfOuswKJf4Dqnmz9bucMsaW0iHL0gob+MjMzNX78eL3wwgtN22bNmqVly5Y122///v0aNmyY4erCJ1gI8eWktTnaquWB5dbjj95t/Rts0UMAAPy1tU6I/3tMsEUP7ebxSPuOW+tYxLmk9P5S/+52V9VcW+uE+D/XwRY9tJvbIxUdkY6etk7cXjrQWfW1pbxK+q+/WF9/d7KUOSD2RkK8oubHqqysVFFRUYuJ6i+++KI8Hk+zWzSHEMkaLdh/PLR1QnxHRj4/Ip2tM1env09LQgtE/iMj+08YLRMAEIV2HQ5tnRD/kZEvjpmtMxiXy3r/m3SpdM1I54UQSdpREto6Ib4jI+VVUnGZ2TqDiXNJlw6wnuurM6IjhEhST5+RnKxBsRtCJId2zWpNSkqKGhuDNIaOEb1TrLMK9Q3SkBAWK8wcaA1B9utmnf2xy21XSJf0/eewYpBRGW8Y+fxIbA41AgDC66bR0oAe0piLg38w84aRT0uky4YaKS+mfGOcNHJAaO/n3jCy95h12RPQHlETRDqa9p4hyXBAF4U4l5Q7LPT9ExMIIQCA0Lja+R6T0IkQcr7i4tr3XCd3JoTg/MTwYA8AAAAApyKIAAAAADCOIAIAAADAOIIIAAAAAOMIIgAAAACMI4gAAAAAMI4gAgAAAMA4gggAAAAA4wgiAAAAAIwjiAAAAAAwjiACAAAAwDiCCAAAAADjCCIAAAAAjCOIAAAAADCOIAIAAADAOIIIAAAAAOMIIgAAAACMI4gAAAAAMI4gAgAAAMA4gggAAAAA4wgiAAAAAIwjiAAAAAAwjiACAAAAwDiCCAAAAADjCCIAAAAAjIu3uwAAAACgo6trkA6ckEpOntv214+lQT2loalSn4vsqy1SCCIAAACATU5XS2/tkjbvlarrmj+2cde5r4f3kSZdKuUMkVwuszVGCkEEAAAAMMzjkQr3S38ulKrrg++/77h1GzVQumuc1L1L5GuMNOaIAAAAAAZ5PNLftkrL3w8thPj67LD0qzeko6cjU5tJBBGEjdsjbTlg/XGForZe+uRgREsKyYET0vEzoe//aYl0ti74fgDgVGWV0r5joe+/75h0oiJy9YTC88/3GLc7tP3rG6VtX0a0pJAcKpcOl4e+/67DUkVN5OoJhdvdvvfz6jppZ0lES4o56z6VNuwKvl9bTldLT6+3/o1mBBGEzZ8/kn7/rvTKR8FfvGrrpWffkp7fJH24z0h5rSo5KT2zXnryzdDCSOF+6XcF1vfUN0a+PgAItzPV0pPrpF9vlPYeDb7/3mPWvk+9ae+Hnte3W+8xKzYHDyP1jdb7y4vvSJs+N1Nfa46esZ63p9aHFkY+OSj99i3re2raeZY8nP74gfVcr94a/P28uk769QbpuQJpqwOCXzQoLpPe+CTwPo/ebd0COV0t/emD0AOjEzk6iLjdbi1dulTp6elKSkrS6NGjVVBQoJEjR2ru3Ll2lwc/WYOl+DjpnT2Bw4g3hOw9JnVPli5JNVpmM30ukgb3sv6Yg4WRwv3S8vckj6TswVJCJ2NlAkDYpCRKaf2sDj2/eStwGNl7TPrNRmvfEf2kixKNldlC5kCpc7z1WhwojHhDyK7DUtdEKa2v2Tp99epqdTuqqg0eRj45KL34tnV1QeZAKdHGWbzZg6U4lzVROlAY8YaQL8uknl2li3ubrTMaeTzSnz60/j+Hw6eHpB1RPBrl6CAyZ84c5efna968eVq7dq1mzJihmTNnat++fcrNzbW7PPjJHCh9e1LgMOIfQu6fKvXpZku5kqTEBGnuZGlE38BhxDeETM+Rbsg2XSkAhEdcnDRzvHTFJYHDiG8IufwS6Zvjre+1y4i+0rwpgcOIfwjJu14a2NOeeiXrhNWca633x0BhxDeETMmUbh5jb1ek7CHS7GsChxHfENKrq5Q3VeqdYk+90WT/8ebtecPhbRtH/S6UY4PIihUrtGzZMq1evVoLFizQlClTtHDhQk2YMEENDQ3Kzc1VbW2tZs2apUGDBqlHjx667rrrtGvXBVxwhwvWWhjxcloI8QoWRgghAGJNsDDitBDi1VoY8XJaCPEKFkacFkK8WgsjXoSQ8/dBBC5H33PUmvcVjRzwstK6xYsXa9q0aZo0aVKz7WlpaUpISFB2drYaGhqUlpamDz/8UGVlZZo6daruuusumyqGl38Y8XJiCPFqK4wQQgDEqrbCiFNDiJd/GPFyYgjxaiuMODWEePmHES9CyPn78kR0HTfSXB6P86a4lJSUaMiQIXruuec0Z86cZo/NnDlTu3fv1tatW1t8X0VFhbp166bq6molJSWZKhdt2HXYmrzW4DN07sQQ4st31KZL53PdsQghAGKV2y39YbP00X7rA7NHUkOjM0OIL9/A5OXEEOLLd9QmKcF6z/HImSHE146D0gtvN5/XQAhpv/pG6f+8HNrkcu9E9QeWh3bs60dJXxtz/rXZxZELGpaUWLNu+vfv32x7dXW1CgoKNH369Fa/77333tOwYcPCGkJcTn1ViBLDc2/R1x78iySprqZSj//7GP330S/sLSqIhMSu+kb+R9LATEnSx2sf0WP3PGRzVQAQOS5XnG5e8JqGjf4XSdKB7W/o8Xtv0r2eEHvl2mTwqOt0+/9dL0lqqKvRsz8ap0UHg7QjslmnhETd+ZN31e8Sa67rZ5te1GP3zLa5quAyxn9D/3L/HyRJtVWntPSBy/TwCdpktUdSSm/N+3XzoYtgnbHaetw/oDzy+DO6+cX7LqC68Ap1nMOR5zlSU602SkVFRc22L1myRKWlpRo7dmyL7ykvL1deXp5+/vOfG6kRwSUkdtWYaQ823e+clKLLpj1gX0EhGn75rerZf2TT/fRxd6pHvzQbKwKAyBqQcZUGjrym6f7AkRM1IONqGysKrlNConKn/3vT/fjOSRo7/d/lcjnyo02ToTnTlDokp+n+sMtuUuoQZw+5d07upsum/VvT/cSuPTT6q3k2VhSd3O6G4Dud77Ebbez3fAEceWmW2+3WmDFjVFpaqqVLl2rQoEFatWqV1qxZo+LiYm3evFnjxo1r2r+6ulo33HCDrr32WoKIQ/hPTP+XHGnVR9ZlWhPTpduvcOYQtO+cEF9Ov6QMAM5Xa5c4SdYcjHmTrba9TuM/Mf3mMdIrhc6d1+LlOyfEl5MvKfOfmD41y3o/d+q8FifzeKT/fMWaJxRMey/N+nquNOnS86/NLg78M5Xi4uK0cuVKZWVlaf78+Zo9e7ZSU1OVl5en+Ph45eScO5PQ0NCgGTNmKD09nRDiEK11xxqfFry1r938J6Z7BWvtCwDRyn9iulew1r52aq071rgRwVv72s1/YrpXsNa+dmqtO9ZV6cFb+6J1Lpe1dlkkDInQcSPNkUFEkjIyMrRx40ZVVVWpuLhY+fn52rFjhzIzM5WcnNy033e+8x253W49++yzNlYLr0AtekNZZ8QugbpjhbLOCABEm9a6Y3mFss6IHQK16A1lnRG7tNYdyyuUdUbsEKhFbyjrjKB1XxkU/mOmJEXvYpKODSKtKSwsbLaQ4Zdffqlly5Zpw4YN6tGjh1JSUpSSkqLi4mIbq+y4QlknxIlhJFiL3lAXPQSAaBGsRW+oix6aFMo6IU4MI8Fa9Ia66KFJoawTQhg5P5dfYv1+htOEEVJ8p/Ae05SoCSKVlZUqKipqNlF96NCh8ng8qq6uVmVlZdPt4osvtrHSjusPm0NbJ8Q/jLy122ydvvYfD22dEP8w8vQG600RAKLNqbOhrRPSWhg5ddZ4uU1e+Si0dUL8w8gbO8zW6etweWjrhPiHkac3SDU2zj3+/buhrRPiH0be3dP6fjgnubM0dVT4jpeSGJ1zQ7wc2b63NSkpKWps5JOfk03LsVb2/NbVwSd1e8PIup3Wtb12GZpq/fd7dQ2+Tog3jDy3Sbo63XrjAIBo06OLNC1bOnwq+KRubxiRpP7dre+1y1e/Ih0qt+oJNqnbG0ZWb5WuGRl430ga0MP678e5gk/q9oaRFzZJlw211hmxy02XSRU10qxrgq8T4g0jmz5vPs8Ibbs+S/qkRCo5eeHHuuNK69KsaOXIrlmIXh5P+7pntHf/SGirBm+nCv8e3k6oGQAuVGuvZU5/3YvW9xiJ5xrNlVdJj6+z/j1fN2ZbXUmjWdRcmoXo0N4XISe8aEVjzQBwodrzWuaU171ofL12uXiu0VLPrtIPvnp+3a46uaRbxkZ/CJGi6NIsAAAAIFb07Co9cKO0/jNp/adSbQjrHQ5LlWZc6cw1Z84HQQQAAACwQac46YavSNeOlLbslz49ZM0dOVNjPR7nkgb2ODenNVrb9LaFIAIAAADYKClBujrDunk8VmfORrfV/a1TDE+kIIgAAAAADuFyhX+tEaeK4YwFAAAAwKkIIgAAAACMI4gAAAAAMI4gAgAAAMA4gggAAAAA4wgiAAAAAIwjiAAAAAAwjiACAAAAwDiCCAAAAADjCCIAAAAAjCOIAAAAADCOIAIAAADAOIIIAAAAAOMIIgAAAACMI4gAAAAAMI4gAgAAAMC4eLsL6Ij+XCgdKrfnvz2op3Tb5fb8twEAAAAvgogNDpVLe4/ZXQUAAABgHy7NAgAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYBxBBAAAAIBxBBF0eI1uyeMJff+GxsjV0h7trcMpdQOxJhr/Fj2e9tXR3tdJnON2W7dQOeH3AzCFIIIOraFReuFt6ZXC0N5kK2ukX/1derco8rUF8tkhafFr0vEzoe2/cZf0+DrpbF1k6wI6mrJK6RevSzsOhrb/3mPSz/9m36K2kvVa99ePpec2SfUhfOitb5SeK5D+8jFhpL3cbmn5+9KKzaGFkeo66Yl10vpPI18b4AQEEXRoh8ql3Yeld4qCh5HKGump9dLhcmnT56G9gUeCxyMV7LY+AD35ZvAwsnGX9aGjuEzac8RMjUBH8fEB6USFdUIjWBjZe0z6zUapvEravNdIea06Uy19tF/adVh6PkgYqW+UXtgkfXZYKtwvna42V2csOHpG2lFiPXfBwkh1nfTrDdKXZdI7e6SaenN1AnZxdBBxu91aunSp0tPTlZSUpNGjR6ugoEAjR47U3Llz7S7PmFU/m6wP//KzkLcjdENTpW9PkuLjAocRbwgpPSX17SbdN1VK6GS8XEmSyyXNuVYa0df6UBAojHhDiCTdNU4afbG5OoGOYGqWNCVTcnsChxFvCKlrkC6/RPr6WLN1+ureRcq7XuqaGDiM+IaQronW9/ToYr7eaDaghzRvstQ5PnAY8Q0hPbtK90+VkhJMVwuY5+ggMmfOHOXn52vevHlau3atZsyYoZkzZ2rfvn3Kzc21uzzEiMyBgcOIfwjJmyp1T7atXElSYoI0d3LgMOIfQiakGS8TiHkul3TzmMBhxD+EfHO8FGfzu+/AnoHDSGshZGBP++qNZiP6BQ4jrYWQ3im2lQsY5dggsmLFCi1btkyrV6/WggULNGXKFC1cuFATJkxQQ0NDUxC5++671a9fP3Xv3l1XXnml3n//fZsrRzRqLYx4OS2EeAUKI4QQwJxAYcSJIcSrtTDiRQgJr9bCiBchBB2ZQ14OW1q8eLGmTZumSZMmNduelpamhIQEZWdnS5IWLlyogwcP6vTp03r44Yd1++2321EuYoB/GPFyYgjxai2M/G0rIQQwrbUwsm6nc0OIl38Y8SKEhJ9/GPEihKAji7e7gNaUlJRo586devDBB1s8VlxcrKysLCUmJkqSRo0aJUnyeDxKSEjQkSNHVFNTo6SkpLDU4nK5wnIcX7cv3KjBmZPb9T0f/vXn2rJmabNt9TWVuvgrU9t1nIKCt/SDG6a063s6mqE5N+p/PfgXxSdYv0MnD+3Sb++/TgtPOXemd0JiV9284HUpc5LWf2Zte/N339Vj9/zO3sKADmjizCXKvel/6/Xt1v1db7+kx++drXs97ejhaljvwV/R7Qs3KPmiPpKks2eOa/ni67Xo4A6bK4s9A0deo1v/9xolJFmp48zxA3r+gcn66Ykvba4MCB9PiC32HHZuxlJSUiJJ6t+/f7Pt1dXVKigoaDE/5O6771ZycrJuvPFG3XfffWELIU5y5S0LNf/ZU81uAzMm2l1WTDq6r1BV5edODZZ+8b7OOjiESFJ9bZUOfLK26X5NVbkO7XrLvoKADmzfx6vV2HCuV/bewlflcXAIkaRTR/eo7NCupvvlpbtVfsTmPuUx6kTxdp3xCR3HDmxRZVmI/Z+BGOPIEZHU1FRJUlFRkaZPn960fcmSJSotLdXYsc3bjSxfvlwvvvii1qxZo4qKirDWEmqia48n1lnXDdth0qTJWvUzGsG3xXdievcuUlWNlDVpjuZ9d45uv9y6/MKJfOeESFJS1576wdN7dP9UqU83++oCOhrfOSFeNz/0qmZfI2UPsa+uQHwnpicnSHJJg0Zeo6f+XqM519rXITAW+U5MT0mSauultCtu10vvNDry0j0g0hz5Kz98+HDl5ORo0aJFeumll7R+/XrNnz9fzz//vCS12jErISFBt9xyi/7nf/5HRUWcxUH7+XfHemhaaK197eYfQqTQWvsCCC//ielewVr72sm/O9b3vyp9f2rw1r5oP//uWA/eKH1vSvDWvkAsc2QQiYuL08qVK5WVlaX58+dr9uzZSk1NVV5enuLj45WTk9Pm99bV1enAgQPmikVMaKtFb7DWvnbz747lFay1L4Dwaq07llco64zYoa0WvcFa+6L92mrRG6y1LxDrHBlEJCkjI0MbN25UVVWViouLlZ+frx07digzM1PJyVbrorKyMq1cuVJnz55VXV2dnnrqKR0+fLjFpVvR7o7/fEtX3vqfIW9H+wRbJ8SpYSRQi95Q1hkBEB7BWvQGW2fEDsHWCSGMhE+wdUIII+jIHBtEWlNYWNjisqwnnnhCAwYMUL9+/fTHP/5Rr7/+etMcEyCYUBcrdFoYCWWdEMIIEHmhrBMSyqKHJoW6WCFh5MKFulghYQQdVdQEkcrKShUVFTUb7ejdu7c2bdqk06dPq7y8XG+//bYmTqSTFEJXVWuFkVDWCfENI8crpEab3iQ8HunoaevrYOuE+IaR6jrpTLWREoEO42SlVB/COiH+YeR4ePuqtEttvVReFdo6Ib5hpLzK+l6E7uw/X3dDWSfEN4yUVUr1BBF0AC5PJNpCISA7u2aN6GtNRsQ5R09LSZ1DX6zwwAlpYA/rzcIubo+075iU1q/59geWW/8+enfz7bX10tEz0sW9zdQHdCR7j0qX9GkZQlr7e/R4pC+OSel+f7umVdRYJ2EG9Aht/9JTVpeni2KvO37ElVVa/4a6WGFxmXVyLCkhcjUBTuHI9r2ASf26t2//YQ648i/O1TKEBJKYQAgBImVEO/4WXS77Q4hkBYr2hIpQAwtaau9q6bxWoyOJmkuzAAAAAMQOgggAAAAA4wgiAAAAAIwjiAAAAAAwjiACAAAAwDiCCAAAAADjaN9rg0EBFo+K5f82AAAA4EUQscFtl9tdAQAAAGAvLs0CAAAAYBxBBAAAAIBxBBEAAAAAxhFEAAAAABhHEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYBxBBAAAAIBxBBEAAAAAxhFEAAAAABhHEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQSIQjX1UsFuyeMJbf+ySumjfZGtKRQfH5COnwltX49H2vS5dLYuoiXBQcoqpcL9oe+/95i052jk6gmF9/e0OsTf0/pGaeMuye2ObF0AEA3i7S4AQPt4PNLzm6SiI9aH+tuvkFyutvcvq5SeelM6WSUlxEuXXWyuVl+fHpJ+/67ULVm6f6rUp1vb+3o80uqt1ge2rQek798gxQX4GRH9auqlJ9dJ5WetD+sT0gLvv/eY9JuNkjzSg9OkAT1MVNnSm59Kr2+XtuyXvnedlNy57X3rG62/3V2HpZOV1t8uAHRkjh4RcbvdWrp0qdLT05WUlKTRo0eroKBAI0eO1Ny5c+0uD7CFyyVNyZTi46R39kivfNT2yIhvCBnaWxrZ32ytvtL6SsP7SqerpSffbHtkxDeExLmk60YRQjqCpATp2kutr1/+QHr/i7b39YaQugYp52KpX4BQG2ljh0m9ukpflkm/3tD2yIhvCOmaGDxoAUBH4OggMmfOHOXn52vevHlau3atZsyYoZkzZ2rfvn3Kzc21uzzANpkDpW9PChxG/ENIsLO1kZaYIM2dLI0IEEb8Q8jsa6TsIbaUCxtMyZRuGWt93VYY8Q0hl18ifXO8FGfjO1nvFClvauAw4h9C8q6XBva0p14AcBLHBpEVK1Zo2bJlWr16tRYsWKApU6Zo4cKFmjBhghoaGloEkZdfflkul0urVq2yqWLArNbCiJfTQohXoDBCCIEUOIw4LYR4BQsjhBAAaJ3L4wl1uqtZ2dnZGjx4sNauXdts+3/8x3/okUceUUVFhRITEyVJZ8+e1fjx4+V2u/XTn/5Ud9xxhx0lA7bYdVh6rkBq8Jn82qur80KIr9p66dm3rA+W3ZOtD2fv7yWE4JyNu6S/fmx9fdc4qW83Z4YQX/4nAL4sO/cYIQQAWnLYy7ilpKREO3fu1J133tniseLiYmVlZTWFEElavHixZs2apdTUVJNlAo7gOzLi5eQQIrUcGfnlG4QQNOc/MvL0emeHEKnlyIgXIQQAWufIrlklJSWSpP79m8+sra6uVkFBgaZPn9607cCBA1q9erUKCwu1evXqsNfiCtSOCHCQSyd+Szd+b5kkqerUEf3wuyP1UHWIvXJtkpDYVfcu2SX1tpLHm899T4/c/Rubq4KTTPrWE7rshvvV6JZK97yvb907Ufd6nN37tkf/DH3z51uVkNhFkvT8Dydo0d7NNlcFAOaEesGVA88pqWlko6ioqNn2JUuWqLS0VGPHjm3a9tBDDyk/P18JCQlGawSc5KLUoRp/20+b7nft0V9XzVhkWz2hGnfbw7qo97nhjytu/U/16Ec7IVgGjpyoUdfOaro/IH2CRk2aY19BIeiUkKhJ9zzSFEIk6dp7H1HnZBtbewGAQzlyjojb7daYMWNUWlqqpUuXatCgQVq1apXWrFmj4uJibd68WePGjdOGDRu0aNEivfnmm5KkyZMn6/7772eOCDoU/+vSp4yS/t+71pyRienB1xmxg//EdLfPq1D3ENYZQezznZju765xzmx/698d654J0sqPnH+pJADYxZEjInFxcVq5cqWysrI0f/58zZ49W6mpqcrLy1N8fLxycnIkSZs2bdLmzZuVmpqq1NRUvfvuu5ozZ44WL15s808AmNFad6zLLg7e2tdOrXXH8grU2hcdh393LK9grX3t1FqL3sxBwVv7AkBH5sgRkbbce++92r59uz755BNJ0pkzZ3TmzLlPK3feeadmz56tb3zjG+rWjdOpiG3BWvT6dtNyyshIWy16H1huPf7/zWjeTYuRkY6ntRa9D/3BeuzRu1t203LCyEiwdUKc2k4bAOzmyBGRthQWFjZbP6Rbt24aPHhw0y0xMVG9evUihCDmhfLBJpRFD00KZZ2QUBY9ROwKZZ2QUBY9NCmUxQpDWfQQADqiqAkilZWVKioqajZR3d9bb73F/BDEPI9Heund0M6u+oeRj/abrdXX9oOhtej1DyMvvN18DgliU0299YE+lBa9/mHkcLm5Ov39fUdoixX6h5FXt5itEwCcyJHte1uTkpKixsZGu8sAbOdySTPHS69tk+6eEPwSD28Y2VYsXT7MRIWtyxkiXZUuZQ4Ivk6IN4wsf1+6MdsKL4htSQnW7/MnB6UZVwZfJ2RKpvWv223v+hxTs6RjZ6Rp2cHr8IaRPxdKXxtjpj4AcLKomiMCIDZ554g8ere9dcCZ+P0AgNgUNZdmAQAAAIgdBBEAAAAAxhFEAAAAABhHEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYBxBBAAAAIBxBBEAAAAAxhFEAAAAABhHEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYBxBBAAAAIBx8XYXAACIvLoG6chpqbZe6hQn9ekmXZRkd1UAgI6MIAIAMaqqVvpwn/TRfqn0lOTxNH+8Rxcpe4g0MV3q192WEgEAHRhBBABijNsjvVsk/W2bNRLSllNnpbc/t27jRki3jpWSOxsrEwDQwRFEACCG1NZLL7wt7S5t3/d9sFf6vFSaN0Ua0CMipQEA0AyT1QEYU13X8vKgQM7WRa6WWFTXID37VvtDiNeps9IT66y5JHbweKzfkVDVNwYe8QEAOBtBBIARFTXSY/+QXvkotDByuFxatFp6pyjytcWK17dLe48F3ufRu61bW87WSS++LTU0hre2YDwe6S8fS4/8XTpdHXz/+kbp+U3Sc5sIIwAQrQgiAIw4cko6USG9syd4GDlcLj21XqqslT47ZM15cJrqOitcNbrtrsSy/7i0aXd4jnXktPSPneE5VqhqG6Q9R6RjZ6Sn3gwcRrwhZNdhqeSkNZIDAIg+jg4ibrdbS5cuVXp6upKSkjR69GgVFBRo5MiRmjt3rt3lAWiH9P7StydJ8XGBw4g3hFTVSpkDpdnXSnEu8/W2ZVuxddb+RyulH78i/eTP0uvbpLO19tb15qdSOPNawW5rvokpSQnSfddb81MChRHfENI1Ucq7XurbzVydAIDwcXQQmTNnjvLz8zVv3jytXbtWM2bM0MyZM7Vv3z7l5ubaXR6AdsocGDiM+IeQOddKCZ3sq9ffa9usy5aKy85tq6qV1n1qhZPKGnvqKq+yRo7CqbZBKjwQ3mMGk5JkBYu2wkhrIWRgT7M1AgDCx7FBZMWKFVq2bJlWr16tBQsWaMqUKVq4cKEmTJighoaGpiAyefJkJScnKyUlRSkpKZo1a5a9hQMIqLUw4uXkELLrsDXqILU+knOiQlr5odmavPYcDe9oSNNxj0TgoEG0Fka8CCEAEFscG0QWL16sadOmadKkSc22p6WlKSEhQdnZ2U3bfv/736uyslKVlZV68cUXDVcKoL38w4iXU0OIZK214QpwiZhH0icl9sxXOHgyuo4bjH8Y8SKEAEBsceQ6IiUlJdq5c6cefPDBFo8VFxcrKytLiYmJRmpxBfrkAeCCDM25UV978K/qlGD9PR/YtkZPzr5N36u3ecJFK+57rlIJiV0D7uPxSFded5f2fPAnQ1VZbvrBKqVdeXuzbYE6YwV6/IHl574+erJGLlfyBVZ3/pJSeuv2hRuVOsQ68XS2okz/74eTtajE8Ex6AEC7eELs1e/IEZGSkhJJUv/+/Zttr66uVkFBQYv5IfPnz1ffvn110003ac+ePQIQHSpPHlJ93blJAGfKitXowBAiSS5XaEM0cXE2DOVE6oSJzSdi6msrdfbUuevD6qtPq7ryhI0VAQDCyZEjIqmpqZKkoqIiTZ8+vWn7kiVLVFpaqrFjxzbblpWVJZfLpcWLF+vmm2/Wjh07FB8fnh8t1EQHoH18J6YP6SWVnpJyrv+e7vve93T7FbZ/Bm7hsb9LB8qCr4FSsHaF+nVfYaaof3r5A+n9L5pv8x3Z8OUdCWnrcV+9uyXa9hroOzG9S2frkixpuBa+VKq8qVJ3+wZqAABh4sgRkeHDhysnJ0eLFi3SSy+9pPXr12v+/Pl6/vnnJanZiMiVV16prl27qkuXLvrv//5vlZWVqaiIFdAAJ/PvjvWDG0Jr7WuniRmBa3K5pLS+Ur/u5mryGtwruo4bjH93rPunSv92Q/DWvgCA6OLIIBIXF6eVK1cqKytL8+fP1+zZs5Wamqq8vDzFx8crJyen1e9zuVzM6QAcrq0WvcFa+9ptzFBp9JDWH3NJSk6Q7hxntKQmI/pG13EDaatFb7DWvgCA6OPIICJJGRkZ2rhxo6qqqlRcXKz8/Hzt2LFDmZmZSk62xuRPnTqldevWqba2VjU1NfrJT36iHj16KCMjw+bqAbQm2DohTg4jcXHStyZK03OkFJ9eGS6XlDNEenCa1M+mhfX6d5eG9wnvMTvFSVcMD+8xgwm2TghhBABii2ODSGsKCwubXZZVX1+vH/7wh0pNTdWgQYNUWFio1atXh21+CIDwCXWxQieHkU5x0g3Z0k+/fm7bf33dWv29z0X21SVJUzLDe7wrh0sXJYX3mIGEulghYQQAYkfUBJHKykoVFRU1m6jep08fbdmyRRUVFSorK9OaNWs0cuRIG6sE0Ja4OCnOFdo6Ib5hpJPD1hORpHifmro5ZNJ09hDpsovDc6zuydLXxoTnWKFyyQp6oawT4htG4lzWDQAQfVwe2kIBMOREhdS9S+iLFR45bV3u5MSpX96uU8HW6zCpskZ67B/S8YrzP0Z8nDTvOim9X/jqClVDo1R+NvTRpcoaayFJkyM3AIDwiZoREQDRL/Wi9q2Y3r+7M0OIU6UkSXlTreftfCR0skai7AghkjXS1J5L3FKSCCEAEM0IIgAQQ3p0sSbOX9POq1SH95H+z03WZXEAAJjArG4AiDGJ8dLtl0vjR0jvFElbDkh1DS33c0nKGCBNTJeyBjPXAgBgFkEEAGLUoJ7SXeOkO66Qjp6WDpVLy9+3Hvv+V63HkxLsrREA0HFxaRYAxLhOcVYXKt91QUb0JYQAAOxFEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYBxBBAAAAIBxBBEAAAAAxhFEAAAAABhHEAEAAABgHEEEAAAAgHEEEQAAAADGEUQAAAAAGEcQAQAAAGAcQQQAAACAcQQRAAAAAMYRRAAAAAAYRxABAAAAYFy83QUAQLQ4XC59dlgqOXlu29PrpUE9pUv6SKMGSvGd7KsPAIBoQhABgCA+L5Xe2CHtP97ysaIj1m3jLumiJGlihnT9KAIJAADBEEQAoA11DdKrW6T3vwht/4oaae0n0tYvpXuukgb3imx9AABEM+aIAEArahuk32wMPYT4OnJaenydtPdo+OsCACBWEEQAwI/HI/3+XWnvsfM/Rl2D9Oxb0vGKsJUFAEBMIYgAgJ8P90k7SwLv8+jd1i2Q2gbpD+9Lbk/4agMAIFY4Ooi43W4tXbpU6enpSkpK0ujRo1VQUKCRI0dq7ty5dpcHIAbVNUirt4bvePuOW3NG7HaiQnp927n7e45YIz8AANjF0ZPV58yZo1dffVU//vGPlZubq/fee08zZ87U8ePH9dBDD9ldHoAYtK1YqqoN7zHfLZJyh4X3mKFyu6U/b5HeKWq+/an10pBe0ncmS92TbSkNANDBOXZEZMWKFVq2bJlWr16tBQsWaMqUKVq4cKEmTJighoYG5ebmSpI8Ho8efvhhDRgwQN26ddPUqVNtrhxANNtyIPzH3HdcKq8K/3FDsXpryxDiVXJSema91NBotiYAACQHj4gsXrxY06ZN06RJk5ptT0tLU0JCgrKzsyVJv/zlL7V161Zt27ZNqamp2r59ux3lAogBHo90sCwyxy4uk3p2jcyx21JRLW36vO3HPbI6fG0vlnIvMVYWAACSHDoiUlJSop07d+rOO+9s8VhxcbGysrKUmJioxsZGPfLII3r22WfVr18/derUSWPHjrWhYgCx4EyNdLYuMscuPR2Z4wby8ZfBJ8q7XNbkfAAATHPkiEhJidWupn///s22V1dXq6CgQNOnT5dkhRKXy6UnnniiKYz87Gc/06233hq2WlwuV9iOBcDZuvcdrlm/2ttsW7DOWG09/sDy5vd/9vNf6F9e/tEFVNd+E+78ma64+f8GfB3zeKR3P9qh+6bmGKwMABDLPCF2Q3HkiEhqaqokqaio+YXNS5YsUWlpadOox+HDh3Xo0CFVV1erpKREzzzzjP71X/9VBw4cMF0ygBjQUF8TuWPXVUfs2G2pqTgR9GSK292o6jMXsGAKAADnyeUJNbIY5Ha7NWbMGJWWlmrp0qUaNGiQVq1apTVr1qi4uFibN2/WuHHjtHXrVo0dO1ZlZWXq1auXJOnmm2/WbbfdplmzZtn7QwCIOh6P9KOVUk198H29IyH+Ix9tmTVRumzo+dd2Pk6dlf7rL8Hb9M4cL40bYaQkAACaOHJEJC4uTitXrlRWVpbmz5+v2bNnKzU1VXl5eYqPj1dOjnUJQXp6uhISEmyuFkCscLmslraRMKR3ZI4bSI8u0oS0th93SUpNkcYYDkgAAEgODSKSlJGRoY0bN6qqqkrFxcXKz8/Xjh07lJmZqeRkq+l9SkqKbrnlFi1evFj19fXavHmzNm3apMmTJ9tbPICoFYkP5Rf3lnqnhP+4obgt99waJi7v7Z9Xa/XpJt13vdTZkbMFAQCxLqrefgoLCzV+/Phm25555hnNmjVLPXv21KBBg/Tiiy9q2LBh9hQIIOrlDrPW3gjl8qxQXZ0evmO1V3wn6d6rpSmZ0ua91nomSQnSZRdLowZJnRx7OgoAEOuiJohUVlaqqKhI9913X7Ptqampeu2112yqCkCsSUyQ/tdl0qqPwnO8i3tLlztgjY7BvaQ7InTZGQAA58ORk9UBwE5uj/TrDVLRkQs7TkIn6aFp0oAeYSkLAICYwqA8APiJc0mzrrmwievxcdKcawkhAAC0hRERAGhDTb308gfS1i/b9329ukr3XCUN7xuZugAAiAUEEQAIYnux9MYOqfRU4P2SEqx2udOyrbkmAACgbQQRAAiBxyPtPy7tOiwdPCmVVVrbuiRKg3pKl/SRRg8hgAAAECqCCAAAAADjmKwOAAAAwDiCCAAAAADjCCIAAAAAjCOIAAAAADCOIAIAAADAOIIIAAAAAOMIIgAAAACMI4gAAAAAMI4gAgAAAMA4gggAAAAA4wgiAAAAAIwjiAAAAAAwjiACAAAAwDiCCAAAAADjCCIAAAAAjCOIAAAAADCOIAIAAADAOIIIAAAAAOMIIgAAAACMI4gAAAAAMI4gAgAAAMA4gggAAAAA4/5/1xqMRgRP8oAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1043.78x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:58.643611Z",
     "start_time": "2019-12-10T21:47:58.241545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BfkwkldIf5k6Cvl2tvXfGPTfAkD6QMcj3iBFPkNh/3NpJYwb0hOzvQpdO/o0YGTPEGLUxpI+198744U1GkBk1yPf2niBx5IxxrIjYUWw0zJ9ihHp/jlNPkHBjfPaJNcImRFxpErq1bXs7dIyKcMDowf5vHxNtj1nnBjvbtn3qgODU0RZRkf4FCI/4WAUIsb+r2nic6sZb1gubyxkiIiJiLwoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKVFWFyAiEi5q6uDQKThyBkorwO2Gzp0gsScMdkKveKsrvJzbDSfK4PBpOHbu0vItX8HAf9QdqdNJMUkhQkTEh7OVsGkf7DgA1bWtb5ecAJnXQmpi6GprjcsNBQfhg/1G6GnurZ3Gn11jYUISZA6HzjGhrVHCn0KEiEgr3G746BtYs8tohfCl8LjxuP5quO8GiI8Nfo0tOVUG/70NDpzyvW1ZNby3Bz7+Bu4fC6kDgl+fdBxqxBIRaYHLDau2Gw9/AkRjnxbB0veMSx6hdug0/P5d/wJEY2VV8EI+bN4XnLqkY1KIkIBxuWHnIePszR81tfD5kaCW5JdDp40zN399UQwXLgavHn/U1cPuw/5vX1EN+44Fr56O6K+7jVYIs0or4PmNUFkTuJp8OXEe/vcmqPJyycWXt3fBtm8DV1NblLfxOD12Fo6eDV494ptChATMmzvgTx/CGzt8B4maWli+BV7eCtsPhKS8FhWfgWUb4Y8b/AsSBQfhxXzjNbX1wa+vJW43vPohrPi70QztS0U1PLcRXtgCXx4NenkdwtfHjT4Q3vxhlvHwprQC/lIQuLq8qXfBax9777MB/tX9xg44XR642vxRXQvPbzCOU39OLo6dNY7r5zYYHUfFGrYOES6Xi7y8PJKSkoiNjSU9PZ38/HyGDx/O3LlzrS5PmkkdAFER8PevvQcJT4D49iR0i4MhzpCW2UTvq2BATzhf5TtIFByE1z4CNzByAERHhqzMJhwOSBto/Lnuc+9BwhMgSs6B8ypjFIF453LBn7cHbn8Fh4xQEmwffwNFpYHZV209vBmi8OMREwUp/Y0WzVc+8B4kPAGisgYGOaFnl9DV6Y+aOqNVdss+4yTJ6pbLYLJ1iMjKyiI3N5d58+axbt06ZsyYwcyZMzlw4ACjR4+2ujxpJqU//DjTe5BoHiAenQK9u1pSLgAx0TB3IlzTx3uQaBwgpqXBbSNDXWlTY4bArAneg0TjANGnK+RMMX7n4t2+EjgV4LPwrfsDu7/m3O7A/4wvj7XtMl97ORzw/VEwOcV7kGgcIFL6Q9Yt1gX65txu2PQl/PINo1X2rV2w8mP45Zvwzm4joHY0tg0RK1euZMWKFaxZs4aFCxcyadIkFi1axIQJE6irq2P06NHU1NQwe/ZsEhMT6d69O5MnT2bfPvUKslJLQcLDbgHCw1eQsFuA8PAWJBQgzNsehP4Ae48a/yfBcug0nAzCF36oLzX6ChJ2DhBgvAfX7L68I25dPWz8ElbtaPl14cy2IWLx4sVMnTqVzMzMJsuHDRtGdHQ0I0eOpK6ujmHDhrF9+3ZKS0uZMmUK999/v0UVi0fzIOFhxwDh0VqQsGuA8GgeJDwUIMw7dDrw+3S7W56rIVAOB6FmgMMBujzSFi0FCQ87B4jzF+D9vd63+fgbIwh1JLYMEcXFxezdu5fp06dftq6oqIjU1FRiYmLo0qULTzzxBImJiURGRvIv//Iv7Nmzh+rqIEZ+8UvjIOFh1wDh0TxILH0P/o+NA4RH4yDhoQBhTkW18X8fDMEcRRCsfVs18qF5kPCwa4AAo9XG5fa+jQMjSHQktpxsqri4GICEhIQmy6uqqsjPz2fatGktvu6jjz5i8ODBxMYGboYXR+NPZmmzoaPv4vsL3gLgYnUFz/zrKP79hL3fRdExXXggdwf0TwFg17qlPP3gYxZX5dvIyT9hctYyAC6UneLJR9NYdC4EPfo6kG59hjL7902vZ/gaydDa+p+91vT5r3P/g1tf/3/aUV3r7vjZmwwb889+1eVrfeO6yyprcTg6tbO69pnyyEukZmYBcPLgTv445yZ+UhvCcbN+mvLIS6Tc/EMiIlv/Wq131fOnVe9z39iWv8PsxO3nWH1btkQ4nUZ3/cLCwibLlyxZQklJCRkZGZe95uzZs+Tk5PDkk0+GpEbxLTqmC6OmLmh43ik2nuun/sy6gvw0dMzd9EgY3vA8adx0uvcdZmFFvsXG9yJtSnbD885de3PdxIctrCg8uerbOKtUG9TXt2PyBh/cQarbFcSa/dFrwHUMGfX9S88HpjEobaqFFbWutqayaXNgS9xu6mouhKagEHG4/Y0bIeRyuRg1ahQlJSXk5eWRmJjI6tWrWbt2LUVFRWzbto1x48Y1bF9VVcVtt93GLbfcohBhE807Ud6eBqt3QJ0Lbk6Ce2/w/X6zQuM+EI3Z+TJM806UNyfDX3Ya1+FvT4Pv2fQyjB3Vu+DxPxsd4XzxnMk3b3FozQ8mwNih5mvz5p3dRsc9f7Sl7n7d4PF/Ml9XezTuRHltP+jbDfK/gggHzP6OMczZTvaXwLJNvrd78EbjEmRHYcuWiIiICFatWkVqairZ2dnMmTMHp9NJTk4OUVFRpKWlNWxbV1fHjBkzSEpKUoCwiZZGYYwf5nv4p9Wad6L08DX800otjcK4Zbjv4Z/SssgISOwenH0PDOIcHcHa94BewdmvL81HYfw4E+7O8D3800pJCdCvu9HvoSUOh/F5mH51KKsKPluGCIDk5GQ2b95MZWUlRUVF5ObmsmfPHlJSUoiLu9RT7OGHH8blcrF8+XILqxUPb8M4/ZlHwireRmH4M4+EFbwN4/RnHglpWTBuQNUr3jiTDpakBIgKQkdDK+5G2towTn/nkbBKhAMeyYSe/7gdfPMw0SUGfjLZfh1C28u2IaIlBQUFTSaZOnz4MCtWrGDTpk10796d+Ph44uPjKSoqsrDKK5c/80DYMUj4Gsbp74RUoeTPPBAKEuZMuMZokQikm5KML5lg6RIDGYMCu89uccbMrKHkax4IuweJnvHwv6bBvWOgf49Ly//pevj5HUZLRUcTNiGioqKCwsLCJp0qBw0ahNvtpqqqioqKiobH1Vd3sPaiMPHf2/wbxtk8SGz5KrR1NnbwlH/zQDQPEs9vsvbeGS9v9W8YZ/MgsfNgSEsNS1fFGZeEAqV7Z7gxKXD7a81t1wX2LPf2tMCHKW+qa40+Bb6GcbYUJOw090JsNHxnuBEmPKakWndb+GCz5RDPlsTHx1Nfb9Gntvhlappxw6Ef3uS7A6InSKzfC+OuCU19LRnkNH5+zy6+54HwBImXthpnllbeO+PODOPeBj/O9D0PhKcT165DkKZ87Zfb0+CLo4GZBfKB8cYXS7A5rzLOeP+ys/37urZf6N+XsdFGOPj0MMzxMQ+EJ0gA1Ls75hl+uLDl6AwJX25320ZdtHX7YGitBk/v9ebj6e1Qs5k67FJ3uDhVBs+sN25PbdbdGTAxJXA1+eJ2GzcPa8+ERv26waO3GpdIrNCW49Tz7WXX47q1z5COJGwuZ0h4aOub2Q5v/nCsGcK37nDRuyv89FZIMNEhMioSZowNbYAA4/94+lij+dzMf3dSX2sDBLTtOHU4dFxbLWwuZ4iIhFrvrrDwdqNT6pav/OsHk9QX7hsLfS2aUyTCYVzWGNEfVm2HkvO+X9O5E0xLN/puBLMDqHQ8ChEiIl5ERcId18OkEbDjAOw7BsVnoOIfMy9HRkD/7jCkt9GPILGHt72FztA+8G93GJ2ddxyEotNwvOzSJYDunY35JUYOhOuvhk76NhATdNiIiPihcyfIvNZ4uN2wYKWxfMn9oR3F0BYOBwzrazzAmJHzYp1Rr0KDBIIOIxGRNmp8Hd6uAaIlkREQZ+39tKSDCaPDX0REROxEIUJERERMUYgQERERUxQiRERExBSFCBERETFFIUJERERMUYgQERERUxQiRERExBSFCBERETFFIUJERERMUYgQERERUxQiRERExBSFCBERETFFIUJERERMUYgQERERUxQiRERExJQoqwu4Er1ZAEfPWvOzE3vAPWOs+dkiItKxKERY4OhZ+Pak1VWIiIi0jy5niIiIiCkKESIiImKKLmeIiGXcbiivhqqLEBEBPTpDVKTVVYm0j8sN5y80eu4yju+OSCFCREKq3gVfHoVPDsCh01BRfWldhAP6dYfURJgwDHp0saxMkTa5WAe7DsHOQ3DkDFTXXlr38z9DYk+4/mq4YSh07mRVlYGnECEiIVN4HF7/BEorWl7vchsdj4+ehfVfwE1J8P3rISY6pGWK+M3tNgLx27uMFrWWXKyHg6eMx98+hdvTIXN4x2idUIgQkaBzueGd3bB5n/+vcbvh74Ww7xg8nGm0UIjYSU0tvPohfHHU/9dcrDcCx54jkHULxMcGr75Q6AA5SKR96l3GF5a/6uqDV0tH5HbD6h1tCxCNlVbAs+vh+PnA1iXSHhfrYPmWtgWIxg6cguc2QGVNQMsKOYUIuaLV1cN/fQBvFPgXJCqq4ffvwYeFwa+to9j2LXz0tfdt/jDLeLTmwkV4Kd/44Baxg7d2+Z7vx9dxXXIe/ntb205i7EYhQq5oR8/CV8eMZnNfQaKiGp7bCMfOwtb9UGvTFgk7fSCdrYS3dgZmX6fKYd3ngdmXSHsUHvcdjP21t9jokBmubB0iXC4XeXl5JCUlERsbS3p6Ovn5+QwfPpy5c+daXV7IrP7NRLa/9Ru/l4v/Bjnhx5kQFeE9SHgCRMk56NMV5k+BaBsNRTxyBl77CP7tdXhsJeS+BRu/aNpD3ApbvoKaALYebN3fdDSHFb49CS9vvfR88TvwwX61klxJ3tsT2P29u8foNxSObB0isrKyyM3NZd68eaxbt44ZM2Ywc+ZMDhw4wOjRo60uTzqIlP7eg0TzAJEzBbrFWVbuZXYegqXvQsFB44vMDZRWwjufwu/fhfIqa+q6WAeffBvYfda7Ar/Ptsj/yuifsefIpWUny4xj5rkN1oc2Cb6Sc4G/bcGpcvj6eGD3GSq2DRErV65kxYoVrFmzhoULFzJp0iQWLVrEhAkTqKurawgRs2bNom/fvnTr1o2xY8fy8ccfW1y5hKOWgoSHnQPEiTKjBcLlNsJDc6fK4DWL3hKHTwfnS/WrksDv0x8HTsJf/nFppvHv2vP3w6XGzfWkYwvW8WfVcd1etg0RixcvZurUqWRmZjZZPmzYMKKjoxk5ciQAixYt4siRI5w/f55f/epX3HvvvVaUKx1A8yDhYdcAAUYHT2/NoG6MD6cTZSErqcGRM8HbrxX9PrbuB4ePbQoOWn+5RYLrSGmQ9huk90uw2XKeiOLiYvbu3cuCBQsuW1dUVERqaioxMTEAjBgxAgC32010dDTHjx+nurqa2NjADL51OHx9bLTdvYs2MyBlYptes/3tJ9m5Nq/JstrqCq6+bkqb9pOfv4Wf3japTa+50gxK+x7/tOAtoqKNY+jM0X288OhkFp2zX3vjD/MK6ZGQ5HO7703/KZ+9/2wIKrok84fPcP1t/9Jkmbee6t7W/+y1S3+vroXY+O5cvBDaMZ/ZL5bRKfYqr9u43DAqczrfbF8doqok1Kb/8u/0T76pyTIzx3XjYxpg554DOG69pp3VBY7bz6Ru2xABkJCQ0GR5VVUV+fn5TJs2rcnyWbNm8cYbb1BTU0NOTk7AAoSdjL1rEWPvfqLJstW/mWhNMR3ciQMFVJ49Rrc+QwEo+eZjLtgwQABERvk3f66/2wVSRETwep4Gc9+t/sxI/6bNtOJ3LaHjCNKxF6z9BpstQ4TT6QSgsLCwSWBYsmQJJSUlZGRkNNn+tdde45VXXmHt2rWUl5cHtBZ/01hbPLs+8B1z/JWZOZHVvwnTbsAh0LgTZbfOUFkNqZlZzHski3vHQBAaptpl+WbYV+K7ef9Py/MYkZjnfaMA+9unxtTVjTU/+/LwnKm1tr6xCAeUnysN+Y26/v+1xvBeX++eDe+8xoCefvxDJCz952ZjFtXGAnFcpyQNCsr3TbDZMkQMHTqUtLQ0nnrqKXr27EliYiKrV69m7dq1AC2OzIiOjuauu+4iPT2dsWPHkpycHOqyJcy1NArj2FljkiNPHwm7BYmbkuHLY62vd2CEoWv7haykBgN6Bme/Cd2tudPnzcnGfT9a43DAwB7B+3eLPQzocXmICITEHoHfZyjYsmNlREQEq1atIjU1lezsbObMmYPT6SQnJ4eoqCjS0tJafe3Fixc5dOhQ6IqVDqG1YZy+hn9aLaW/cWfAljgwvtjuH2fNjX4GOX13RDRjiDMIO/XDDUPgmj4tr3NgHCP3jQ1pSWKBIb3Da7/BZsuWCIDk5GQ2b97cZNlDDz1ESkoKcXFGF/nS0lI2bdrEHXfcQVRUFC+88ALHjh277HJHuLvviS1tWi5t42seCE+QsGOLRIQDHroJesXDB4VNJzxK6A7/PBqSE1p9eVB172z87ry1lJgxflhg9+evqEiYN8m4edIn30Kd69K6q53GMXF1L2tqk9AZ3s84ts9dCNw+Y6Jg1KDA7S+UbBsiWlJQUMD48eObLHv22Wd5+OGHiYiI4LrrruNvf/tbQ58KEV/8nUjKzkEiMgK+PwpuGwmPv24se2wqDOxpfX2Z1wY2RFzTx/h3WaVTFEwfC3ekG/2a6lzQtyv0D9OmaGm7yAi4ZTis2R24fY4fBrFherv7sAkRFRUVFBYWMn/+/IZlvXr1YuvWrV5eJeJdZY0RJPyZB6JxkDhVbsyeaMW1+dbENHo32+WMeHg/GDPEmD+hvaIiYIZNLhd0joGRA62uQqySea0xU+zRs+3fV48ucHvrV+htL2xCRHx8PPX1Nr3jkYStvt3g0SkQ28m/iaRS+sOjt0L/7vYKEHZ2z2goKjWmh26NP73X7xlj/H+JWC0yAh68EZ5+3/usrL6O66gIeOjG8G2FAJt2rBQJpb7d2jYT5WCn0awt/ukcA/O/a7T2mHVXBtzoe04tkZDp1x1+MhniTE4LEh1ptGwObaWzbrhQiBCRoOveGRZ8D8a3cUK+Hp0hezJMSglOXSLtMdgJ/+v2tndevroX/OvtRstmuNP5lIiERFwneGA8jLvGuA/FZ0Wt3/fDeRXcnBTeHc7kytAz3gi6nx+BD7+GQi+T2w5yGsd1xmDjkkhHoBAhIiE1pLfxuHARis8YndPe3mWse/BGY/RF767G8FWRcOBwQPrVxuN8FRSXQsl5Y8h1VCQkdDOO6x5drK408BQiRMQSnTsZzcDJCZdCxJgh1tYk0l7d4qDbAEgdYHUlodFBGlREREQk1BQiRERExBRdzrCAlTdaCdebvIiIiP0oRFjgnjFWVyAiItJ+upwhIiIipihEiIiIiCkKESIiImKKQoSIiIiYohAhIiIipihEiIiIiCkKESIiImKKQoSIiIiYohAhIiIipihEiIiIiCkKESIiImKKQoSIiIiYohAhIiIipihEiIiIiCkKESIiImKKQoSIiIiYohAhIiIipkRZXYCItE9lDXxVAsVnLi179e/QvwcMdsLQPhDhsK4+Eem4FCJEwtSpcnh/L+w+BHWuput2HTYeAL3i4TvJ8J3hEKm2RxEJIIUIkTDjdsPW/fDXT6G23vf2pRXw1i7YcRBmTTBaKEREAkHnJSJhxOWGVdvhLzv9CxCNHT0LT78P354ITm0icuVRiJCAqauHzfug3uV7W4CKaviwMLg1dTTrPoOPvjH/+po6WL4Fjp8PWEkicgVTiJCA+Z9P4O1d8KcPfQeJimp4biOs2gEf7A9NfeHu4CnY8IX3bf4wy3h4U1MHKz/2P+yJiLTG1iHC5XKRl5dHUlISsbGxpKenk5+fz/Dhw5k7d67V5Ukz30mG2Gj4tMh7kPAEiJJz0KcrpF0d0jLDktttBC53gPZXVAqffBugnbVDdS183Khl5ciZ1rcVEfuxdYjIysoiNzeXefPmsW7dOmbMmMHMmTM5cOAAo0ePtro8aWaQE7Inew8SzQNEzhToFmdJuWHl4Ck4djaw+/yg0AgnVvnwa/jlm/D6J5eW/W4dPPM+lFVZV5eI+M+2IWLlypWsWLGCNWvWsHDhQiZNmsSiRYuYMGECdXV1l4WI119/HYfDwerVqy2qWMB7kFCAMG/HwcDvs+Sc0dnSCp98a3QQvVh3+bqDp+C5DVBTG/q6RKRtbBsiFi9ezNSpU8nMzGyyfNiwYURHRzNy5MiGZRcuXODJJ58kNTU11GVKC1oKEucvKEC0x+HS4Oy3KEj79aauHt7Z3fp6N3CiLDjBSUQCy5Yhori4mL179zJ9+vTL1hUVFZGamkpMTEzDssWLFzN79mycTmcoyxQvmgeJxX9VgDDL5YLj54Kz72NB2q83+45BRY33bRw07SshIvZky8mmiouLAUhISGiyvKqqivz8fKZNm9aw7NChQ6xZs4aCggLWrFkT8FocDs0X3B4Dr5vCPT9fT3Ut1Fwo46mfprLoTLHVZYWVqE5x5Lx8ockyXyMwWlv/s9eaPl/+0gqmj51tvjgT0m7NYdKP/uh1GzfwzZFSHA6dGIhYwe1nhylbtkR4WhQKC5tOIrBkyRJKSkrIyMhoWPbYY4+Rm5tLdHR0SGsU32Lje3HLD37X8Dymc1dumfU7IiJtmV1ty1VvdA7w903dFvW1PpoEguDiBd+TVLjdbr+2ExFr2fLTfOjQoaSlpfHUU0/Rs2dPEhMTWb16NWvXrgVo6FS5adMmysrKuPPOO4NWSzA+uK8EjTtResRGQ/K4GcyYPoOHbtJ9HNoi920orbjUKta8RcHD0wLR2vrmnlg4l40vhXa49IUaY1RG8/t9NOZwOLhv0lD+S+8/EVuz5cd4REQEq1atIjU1lezsbObMmYPT6SQnJ4eoqCjS0tIA2Lp1K9u2bcPpdOJ0Ovnwww/Jyspi8eLFFv8LrmzNR2F4+Br+Ka0b2DM4+x0QpP160zkGbkpufb0DiInyvo2I2IMtWyIAkpOT2bx5c5NlDz30ECkpKcTFGb3yHnvsMR5++OGG9dOnT2fOnDk88MADIa1VLmlpGOev3jTWeTpbLttkBAlALRJ+Sht46XcWKFfFwhCLuhzcOQrKLsDuIiM0NG5viImGuROhe2drahMR/9k2RLSkoKCA8ePHNzzv2rUrXbteOtWNiYmhZ8+eTZZJ6PgzD4SChDlpA40v/fLqwO1z/DUQFRm4/bVFZAT88Ga4+aRxL5CTZRAdCSMHwtih0CXG9z5ExHphEyIqKiooLCxk/vz5rW6zZcuW0BUkl/nzdv+GcTYPEgN6whRN8eFVVCTccT38z7bA7O+qWJiYEph9meVwwDV9jYeIhKewCRHx8fHU17fx3scSUveMMfo5zBjnex4IT5DYvA8yrw1NfeFu3FD4vAi+PNb+fd0/Tmf7ItJ+DreGH0iQeUYK+JrbQHyrugjPb2zfjaruzrC+FUJEOgZdiRYJI3GdYP534boBbX9tpyiYOV4BQkQCJ2wuZ4iIIa4T/PgW2HkI1n4GZyq9b+8ARiQal5t6xYeiQhG5UihEiIQhhwPGDIGMQbCvBPYWQ/EZOFVu9EuJi4b+PYy+JzcMAedVVlcsIh2RQoRIGIuIgNRE4yEiEmrqEyEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKESIiIiIKQoRIiIiYopChIiIiJiiECEiIiKmKETYVF09XKzzf/uqi+ByB68ef124GNztRUTEPhQibKiuHv7rA3hhi39BoqIanlkPq7dbGyS+OAr//hbsL/Fv+8374D/+CifKglqWiIgEiUKEDZ2vgiOl8PUJ30Giohqe2wgl5+Cbk1Bt4Zn9V8eguhZezPcdJDbvg7d3QVkVFJ0OTX0iIhJYtg4RLpeLvLw8kpKSiI2NJT09nfz8fIYPH87cuXOtLi9oesVDzq3QNdZ7kGgcIPp0hZwp0Dkm1NVe8s9jYMIwqK33HiQ8AQLg/nFww9DQ1SgiIoFj6xCRlZVFbm4u8+bNY926dcyYMYOZM2dy4MABRo8ebXV5QdW3q/cg0VKA6BZnVbWGCAdMH+s9SDQPEBOGhb5OEREJDIfb7bZBd7zLrVy5klmzZrFlyxYyMzMblt977728+eab7NixgzFjxjBx4kQ++eQTIiMjAbjvvvt45ZVXLKo68E6UwXProawakvrCIxONMGG3ANGYyw2rtsPH30B0pBEoAO7KUIAQEelIbBsiRo4cyYABA1i3bl2T5Y8//jhLly6lvLycmJgYJk6cyKOPPsp9991nUaXB1zhIDOltjMQ4ft6eAcKjcZBoTgFCRKRjiLK6gJYUFxezd+9eFixYcNm6oqIiUlNTiYkJzcV/h8MRkp/jS/d+ydz3xFYO0heAM8e+4oVHJ7Ho3HGLK/PC4WDynGWMnDyvYdGGFx/m6QdfsrAoERHxxd/2BVv2iSguLgYgISGhyfKqqiry8/Mv6w+RnZ1Nnz59uOOOO/j6669DVmcoVZeXcrHyXKPnp7l44bx1BfnD7eZsyaX/j/q6i5SdOmRdPSIiElC2bIlwOp0AFBYWMm3atIblS5YsoaSkhIyMjCbLUlNTcTgcLF68mDvvvJM9e/YQFRWYf5odrvY07kTp0X/4zfzhbxd4ZCJ0suX/YtNOlEP7wIGTnbj/iQ08nAnD+1lbm4iItJ8t+0S4XC5GjRpFSUkJeXl5JCYmsnr1atauXUtRURHbtm1j3Lhxl73O7XbTt29ftmzZwogRIyyoPPCaj8I4+Y+JmbrGNu1sabcg0XwUxrhrmna2VJAQEQl/trycERERwapVq0hNTSU7O5s5c+bgdDrJyckhKiqKtLS0Fl/ncDhs04chEFoaxunhzzwSVmlpGKc/wz9FRCS82DJEACQnJ7N582YqKyspKioiNzeXPXv2kJKSQlycMRzh3LlzrF+/npqaGqqrq/nlL39J9+7dSU5Otrj69vM1D4SveSSs4m0eCAUJEZGOxbYhoiUFBQVNOlXW1tby85//HKfTSWJiIgUFBaxZsyZg/SGs4u9EUnYLEv5MJKUgISLScYTNt21FRQWFhYXMnz+/YVnv3r3ZuXOnhVUFh8MBkQ7/5oHwBInn1kOExZEwKgIcwAwf80B4ggTAzoPW1y0iIubYsmOlQGUN1Luga7MA8bPXjD//MKvp8tPlxrZWd7A8fh4Suvm3rcttdBT1d3sREbGXsGmJuNJ0aeNcWs6rglNHW7UlEEQ4FCBERMKZGpJFRETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIm/riKOw65P/2W/fD4dNBK0dEROQyUVYXIJc7WQYvbwWX23ieMdj79pv3wdu7IK4TPHEndIkJeokiIiL2bolwuVzk5eWRlJREbGws6enp5OfnM3z4cObOnWt1eUHT+yqYkgpuN/zpI+8tEp4AAXDnKAUIEREJHVu3RGRlZfGXv/yFX/ziF4wePZqPPvqImTNncurUKR577DGrywsahwOmjjT+/t4eI0jA5S0SjQPE/eNgwrCQlSgiImLfELFy5UpWrFjBli1byMzMBGDSpEns2rWLN998k9GjRwPgdrv59a9/zfLly6msrGTs2LFs2LDBytIDorUg4aEAISIiVrNtiFi8eDFTp05tCBAew4YNIzo6mpEjjW/Y3/3ud+zevZtPP/0Up9PJZ599ZkW5QeEtSChAiIiI1WwZIoqLi9m7dy8LFiy4bF1RURGpqanExMRQX1/P0qVL2blzJ3379gUgIyMj1OUGVfMg0ZgChIiIWMm2IQIgISGhyfKqqiry8/OZNm0aYAQKh8PBs88+y/Lly+nbty+/+c1vuPvuuwNWi8PhCNi+2uvuf3uPQWm3AbBn03KefnCexRWJiEhH5Ha7/drOlqMznE4nAIWFhU2WL1myhJKSkobWhmPHjnH06FGqqqooLi5m2bJl/OhHP+LQoUOhLjnoRt3+WEOAAEid+GOSx99vYUUiInKlc7j9jRsh5HK5GDVqFCUlJeTl5ZGYmMjq1atZu3YtRUVFbNu2jXHjxrF7924yMjIoLS2lZ8+eANx5553cc889zJ4929p/RAA17kQ5YyycrzIubTgc8NCNvueREBERCQZbtkRERESwatUqUlNTyc7OZs6cOTidTnJycoiKiiItLQ2ApKQkoqOjLa42uJqPwrgxyegj8b2R/s0jISIiEiy2bIlozUMPPcRnn33G559/3rBs+vTpDB48mKeeeoqdO3cydepUPv30UwYPHmxdoQHibRin2w3v7lGLhIiIWMeWHStbU1BQwPjx45ssW7ZsGbNnz6ZHjx4kJibyyiuvdPgAAf5PSCUiIhIsYRMiKioqKCwsZP78+U2WO51O/vrXv1pUVXCcLIN3dht/9zaMs3mQ+PN2GN5PU1+LiEhohNXljCvJ7sNQXev/PBDv74UhvSGpb3DrEhER8VCIEBEREVNsOTpDRERE7E8hQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkRERExRiBARERFTFCJERETEFIUIERERMUUhQkREREz5v/pPq+P2GoBsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 682.577x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookahead_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:58.935337Z",
     "start_time": "2019-12-10T21:47:58.646318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 802.977x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stochastic_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preset Pass Managers\n",
    "\n",
    "Qiskit comes with several pre-defined pass managers, corresponding to various levels of optimization achieved through different pipelines of passes. Currently ``optimization_level`` 0 through 3 are supported; the higher the number, the more optimized it is, at the expense of more time. Choosing a good pass manager may take trial and error, as it depends heavily on the circuit being transpiled and the backend being targeted.\n",
    "\n",
    "Here we illustrate the different levels by looking at a state synthesis circuit. We initialize four qubits to an arbitrary state, and then try to optimize the circuit that achieves this.\n",
    "\n",
    "- ``optimization_level=0``: just maps the circuit to the backend, with no explicit optimization (except whatever optimizations the mapper does).\n",
    "\n",
    "- ``optimization_level=1``: maps the circuit, but also does light-weight optimizations by collapsing adjacent gates.\n",
    "\n",
    "- ``optimization_level=2``: medium-weight optimization, including a noise-adaptive layout and a gate-cancellation procedure based on gate commutation relationships.\n",
    "\n",
    "- ``optimization_level=3``: heavy-weight optimization, which in addition to previous steps, does resynthesis of two-qubit blocks of gates in the circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:58.956270Z",
     "start_time": "2019-12-10T21:47:58.937115Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "from qiskit.test.mock import FakeTokyo\n",
    "\n",
    "backend = FakeTokyo()     # mimics the tokyo device in terms of coupling map and basis gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:47:59.632459Z",
     "start_time": "2019-12-10T21:47:58.959187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     »\n",
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       "«q_0: ┤0                                                                           ├\n",
       "«     │                                                                            │\n",
       "«q_1: ┤1                                                                           ├\n",
       "«     │  initialize(0.5j,0.35355,0,0,0,0,0,0,0.35355,0.35355j,0,0,0,0,0.5,0.35355) │\n",
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       "«                                                                                   </pre>"
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       "     »\n",
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       "q_2: »\n",
       "     »\n",
       "q_3: »\n",
       "     »\n",
       "q_4: »\n",
       "     »\n",
       "q_5: »\n",
       "     »\n",
       "q_6: »\n",
       "     »\n",
       "q_7: »\n",
       "     »\n",
       "q_8: »\n",
       "     »\n",
       "q_9: »\n",
       "     »\n",
       "«     ┌────────────────────────────────────────────────────────────────────────────┐\n",
       "«q_0: ┤0                                                                           ├\n",
       "«     │                                                                            │\n",
       "«q_1: ┤1                                                                           ├\n",
       "«     │  initialize(0.5j,0.35355,0,0,0,0,0,0,0.35355,0.35355j,0,0,0,0,0.5,0.35355) │\n",
       "«q_2: ┤2                                                                           ├\n",
       "«     │                                                                            │\n",
       "«q_3: ┤3                                                                           ├\n",
       "«     └────────────────────────────────────────────────────────────────────────────┘\n",
       "«q_4: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   \n",
       "«q_5: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   \n",
       "«q_6: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   \n",
       "«q_7: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   \n",
       "«q_8: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   \n",
       "«q_9: ──────────────────────────────────────────────────────────────────────────────\n",
       "«                                                                                   "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(10)\n",
    "\n",
    "random_state = [\n",
    "    1 / math.sqrt(4) * complex(0, 1),\n",
    "    1 / math.sqrt(8) * complex(1, 0),\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    1 / math.sqrt(8) * complex(1, 0),\n",
    "    1 / math.sqrt(8) * complex(0, 1),\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    1 / math.sqrt(4) * complex(1, 0),\n",
    "    1 / math.sqrt(8) * complex(1, 0)]\n",
    "\n",
    "qc.initialize(random_state, range(4))\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now map this to the 20-qubit Tokyo device, with different optimization levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:00.000884Z",
     "start_time": "2019-12-10T21:47:59.634920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gates =  OrderedDict([('cx', 55), ('u3', 15), ('u1', 15), ('reset', 4)])\n",
      "depth =  69\n"
     ]
    }
   ],
   "source": [
    "optimized_0 = transpile(qc, backend=backend, seed_transpiler=11, optimization_level=0)\n",
    "print('gates = ', optimized_0.count_ops())\n",
    "print('depth = ', optimized_0.depth())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:00.474954Z",
     "start_time": "2019-12-10T21:48:00.003129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gates =  OrderedDict([('cx', 55), ('u3', 15), ('u1', 6)])\n",
      "depth =  59\n"
     ]
    }
   ],
   "source": [
    "optimized_1 = transpile(qc, backend=backend, seed_transpiler=11, optimization_level=1)\n",
    "print('gates = ', optimized_1.count_ops())\n",
    "print('depth = ', optimized_1.depth())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:01.649048Z",
     "start_time": "2019-12-10T21:48:00.477272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gates =  OrderedDict([('cx', 45), ('u3', 15), ('u1', 6)])\n",
      "depth =  55\n"
     ]
    }
   ],
   "source": [
    "optimized_2 = transpile(qc, backend=backend, seed_transpiler=11, optimization_level=2)\n",
    "print('gates = ', optimized_2.count_ops())\n",
    "print('depth = ', optimized_2.depth())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.166110Z",
     "start_time": "2019-12-10T21:48:01.651535Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gates =  OrderedDict([('cx', 20), ('u3', 15), ('u1', 6)])\n",
      "depth =  38\n"
     ]
    }
   ],
   "source": [
    "optimized_3 = transpile(qc, backend=backend, seed_transpiler=11, optimization_level=3)\n",
    "print('gates = ', optimized_3.count_ops())\n",
    "print('depth = ', optimized_3.depth())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing the DAG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Qiskit, we represent circuits internally using a Directed Acyclic Graph (DAG). The advantage of this representation over a pure list of gates (i.e., *netlist*) is that the flow of information between operations are explicit, making it easier for passes to make transformation decisions without changing the semantics of the circuit.\n",
    "\n",
    "Let's start by building a simple circuit, and examining its DAG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.375950Z",
     "start_time": "2019-12-10T21:48:03.169405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 381.577x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from qiskit.dagcircuit import DAGCircuit\n",
    "q = QuantumRegister(3, 'q')\n",
    "c = ClassicalRegister(3, 'c')\n",
    "circ = QuantumCircuit(q, c)\n",
    "circ.h(q[0])\n",
    "circ.cx(q[0], q[1])\n",
    "circ.measure(q[0], c[0])\n",
    "circ.rz(0.5, q[1]).c_if(c, 2)\n",
    "circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the DAG, there are three kinds of graph nodes: qubit/clbit input nodes (green), operation nodes (blue), and output nodes (red). Each edge indicates data flow (or dependency) between two nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.525267Z",
     "start_time": "2019-12-10T21:48:03.378085Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=453x639 at 0x7F1EEB707AF0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.converters import circuit_to_dag\n",
    "from qiskit.tools.visualization import dag_drawer\n",
    "dag = circuit_to_dag(circ)\n",
    "dag_drawer(dag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, writing a transpiler pass means using Qiskit's DAGCircuit API to analyze or transform the circuit. Let's see some examples of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**a. Get all op nodes in the DAG:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.532050Z",
     "start_time": "2019-12-10T21:48:03.527373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<qiskit.dagcircuit.dagnode.DAGNode at 0x7f1eebb870b0>,\n",
       " <qiskit.dagcircuit.dagnode.DAGNode at 0x7f1eebb87820>,\n",
       " <qiskit.dagcircuit.dagnode.DAGNode at 0x7f1eebb87eb0>,\n",
       " <qiskit.dagcircuit.dagnode.DAGNode at 0x7f1eebb876d0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dag.op_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each node is an instance of the ``DAGNode`` class. Let's examine the information stored in the second op node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.540054Z",
     "start_time": "2019-12-10T21:48:03.534378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node name:  rz\n",
      "node op:  <qiskit.circuit.library.standard_gates.rz.RZGate object at 0x7f1eec144e20>\n",
      "node qargs:  [Qubit(QuantumRegister(3, 'q'), 1)]\n",
      "node cargs:  []\n",
      "node condition:  (ClassicalRegister(3, 'c'), 2)\n"
     ]
    }
   ],
   "source": [
    "node = dag.op_nodes()[3]\n",
    "print(\"node name: \", node.name)\n",
    "print(\"node op: \", node.op)\n",
    "print(\"node qargs: \", node.qargs)\n",
    "print(\"node cargs: \", node.cargs)\n",
    "print(\"node condition: \", node.condition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**b. Add an operation to the back:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.660392Z",
     "start_time": "2019-12-10T21:48:03.542892Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=532x639 at 0x7F1EEB7F46D0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import HGate\n",
    "dag.apply_operation_back(HGate(), qargs=[q[0]])\n",
    "dag_drawer(dag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**c. Add an operation to the front:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.773434Z",
     "start_time": "2019-12-10T21:48:03.662725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=532x755 at 0x7F1EEB70C790>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import CCXGate\n",
    "dag.apply_operation_front(CCXGate(), qargs=[q[0], q[1], q[2]], cargs=[])\n",
    "dag_drawer(dag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**d. Substitute a node with a subcircuit:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:03.905653Z",
     "start_time": "2019-12-10T21:48:03.776373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F3DvFuBSwK5NtZtYtyJ03m1SxQoiHuq/IyJcvH7obDhjQ0PTO13X/efw6GZ8JUhfbId/F2D6M01iA+UBRHDM75mHufPg74hfa3TZv2Llhe+4TF6W7oXNI4QeQN19exxRj9t4vn2BbV2Y5rnHhtnAIzuuYfSKEcC33FRlPP/00KadT7P8X2hhs104YCmzA9lfaEaA3tpUIM0KPbeGnyDvffx1b3kHAKWAqtsPNzrQbfAJ8KF68uN2bLlWqFN7+3rDHzg3bc5+4opuQfC6ZSpUqOaT5qs9UxbjHASuP2XO/zAJGAbuxHSHR/ed+xh6BM8a0x0S1StWc37EQwunuKzJeeOEF239st3NPJYGdQDVsK3Xmx3YIdwS2Q7YZFQLsAG4DZYBi2AqMLdw/YM4J9Fv11Hm+DgaD/U92G41GatasiW6rnY8w2XufrOKfX15XsB1ZSvu3FtNgt4LBYOD55593SPP169VH7VCQYueG7blfFts3WpYlgXWnlXp162mdRAjhBPf9GZY7d26q1KjCgQUHsLa28wDQ0sBSO7ZXGdsVP7UWA/o1epqPdtw5mhZNWrBt1DZS41IffPXTzLDnPmmB85aofgyG+Qaq1axGQECAQ9pv1KgRKknBSuBlOzdur/3ixOX1H8tyUKmK0NBQrZMIIZwg3WXFXx/wOizhn5ka4uFmgUmZ6NGjh8O66NWrFwazARy3cnn2cgXUCsWQ14Y4rIsnnniCJs2aYJpk70Vlsi/TRBMtW7WkcOHCj36xEMLtpVtkdOzYkcCgQHTjnL2iVToGYjvcnpW/3ofzz2F7ey8Bkgimr0307N6TXLly2bnxf+TOnZtuXbphGm+6d7CrFlx9nwC6z3TkyZuHtm3b2r/xfxn6+lDMv5vhT4d283hcfb9sBfOfZoYMdlzhJ4RwMQ+6CPzUqVOV3qhXHEQ57PYLCv5z7+PA/hxx+wjl7e+trly58qBNaTfXrl1TPgE+iv858P1kh31yCKU36tVPP/3k8H2ilFKNmjZSpoomhdmB78nd94sZZXrGpEIbhzplnwghXMJ8nVLpX3PZarVSrVY1DukOkbo11XWvyKqlv8EQYmDcmHG8+eabTuly3LhxjBg5Assei+0KmuJeyWB83shzHs+x649d6HSOPxp38uRJKj5TEfNIs21wprjf/4FpjInjR45TsmRJrdMIIZxjQbqnSwD0ej1zZ83F46gHuqEucNrE1USCqY2JWjVr8frrrzut26FDh1KjWg1MbUz/XChO3KUbrMPzlCc/z/zZKQUGQOnSpfli7BfoPtLZppyKe60F3Sc6vhr3lRQYQuQwDywyAMqWLcucn+bYLqQ00UmJ3EEiGNsZCbYEs2TBEoxGB6yV8AAmk4lfF/5K7sTcGDoYtB+f4Uq+AmbAvNnzKF3auevKDx06lI4dO2LsbLStaSFsDoGxq5Gu3boyePBgrdMIIZzsoUUGQJs2bfj8889hCPC9ExK5ukQwtDLgc8iHtSvWOmzJ6ofJnz8/a1esxWefD8Y2Rik0AL4F3obxX46nZcuWmkSYPnU6NSrWwNTQZFthM6c7DMZQI7Uq12LKD1O0TiOE0MLjjt747LPPFDoUY1BYNR9Gps3tFspYz6gCggLUgQMHHDdU5jHt27dP+ef2V4aGBkWE5ltHm5sV20BYHWrcuHFa7xIVFxenar1QS5nymRR/ar51tLttRxmDjapO/ToqPj5e690ihNDG/McuMpRSauLEicpgNChDJ4MiQfOPMefeDqNMJUyq0JOF1N9//+2oHZJhf/31lypYtKDyKOWhOKr5VnLuLR6lb69XBpNBff/991rvirtiY2NVi5YtlMHToJiu+VZy/m0KSu+hVy3btJQCQ4icLWNFhlJKbdy4UQUEBShTBZNir+YfZ46/WVF8hzL4GlStF2qp8PBwR+yILLl+/bqqUbuGMvgZFJPJGUea9qBM5U0qMDhQbd68WetdcB+LxaI++OADpdPplL67XhGp+RZz/C0Cpe+qVzqdTn388cfKarVqvRuEENrKeJGhlFLnzp1TderXsa2j8QHZ96jGaZSxgVHpjXr14YcfqpSUFHvvALtJTk5WI0aMUAajQZlCTYozmm89x9wSULyP0hl1qm5oXXXhwgWtN/1DrVy5UuUtmFeZCpkUyzXfeo67LUMZCxpV3kJ51erVq7Xe7EII15C5IkMppaxWq/ruu++Ut5+3Mj1hUvyMwqL5R519bhEo3rQd8i1TsYzau3evPTe6Q+3evVs9Vf4ppffUK95GEaX51rTPzYJiFspU1KS8/bzV999/7zZ/KUdERKgu3booQBlDjY5d4M7Zt/22QhxQXbt3VZGRkVpvbiGE68h8kZHm6tWrqk/fPkpv0CtTZZNiAYpUzT/6MncLRzESZcxtVLnz5VaTJk1SZrPZHhvaqVJSUtTEiRNVYHCgMuYx2gZG3tJ862buZkYxz7ZapN6gV3379VXXrl3TehNnyrZt21Tl6pWVTq9T+i56xV+ab93M3/ah9B31SqfXqSo1qqjt27drvXmFEK4n60VGmsOHD6uX279sKzaKmxTf4D6/2P5G6QbqlMHboALyBKiRI0eq27dv22vTaCY6Olp99NFHKiAoQBl8DEo3SKc4rPnWfrzbTRRfoUzFbMXFyx1eVkeOHNF6k2aZ1WpV8+fPVxUqVVCA7dTWShy7JLm9bikolqMMDQwKUBWeraAWLlzoNkeUhBBOZ78iI83p06fVa4NeU16+XkrvoVeGNgbFMhSJmn9E3nu7guJrlOlZkwLUk6WeVBMnTsyWo+Hj4uLUhAkTVNESRW2/2J4zKSaguKb5Xrj3loDiV5ShtUHpPfTK289bDR48WJ05c0brTWh3VqtV/fbbb6peaD2l0+mUKb9J8SaK/ZrvhXtvVmwDvIegTPlMSqfTqQYvNlDr16/XehMKIVzfg69dklVxcXEsWbKEGbNnsH3LdvReemgIlhYWaAQUd0SvD2EG9gNrwWO1BykHUvDx96FT+0706tmL559/3mnLUGtFKcW2bduY9dMsFixeQGJcIqYqJswtzNAUeA5w3uKlNmeBDWBYZYDNYE2yUrdBXV7p8Qpt27bF19fXyYGc79y5c/z8889M/3k6F09fxKOIBynNUqA5UBdw3MV90xcNbAVWg2m1CfNVM08+9SR9uvehR48ePPnkk04OJIRwUwscVmT829WrV1m9ejUrVq9g48aNJMUnYSpgwhJiwRpihWeAssCTPMYapI8hEThx574fTDtNWPdbsSRaKPBEAYrkK4LBYGDjxo34+WXlutjuKzExkU2bNrF69WqWrl7KjUs3MPgY0FfRY65phipAmTt3Lzt0aAXOA2HAIdDv0mPYZcB8w4yXrxeNGjWiZfOWNG/enIIFC9qhQ/ejlGLUqFFMnTqV4ILBHP7rMAAe5TxIDkmGGkC5O/c8dur0FnD8zn03eO7yJCUsBYDK1SrTpkUbmjVrxnPPPWenDoUQOYhziox/S0pKYt++fezcuZM/dvzBjj07uHX1FgAGbwPGUkZSC6diyWeBAkAQtr/k9IAfYALigRRsy2nHAuHADfC45gGXwHzBjLIqDEYDJcuWpG7NutSqVYuQkBDKli3L3r17qVevHv379+err75y5tt3WcePH2fnzp3s2LGDbbu2cfbEWSypFnR6HR7FPFBFFCmFUiAftrs/tuLDA/DFtj/isRUTt4FI4DoYw40YLhswnzZjTbICkLdwXmpVr0Wd2nUICQmhatWqeHp6avG2Xcq2bdt48cUXefvtt/n000+5efMmO3bsYOfOnWzbuY2DBw6SGJcIgCnYhK6UjtR8qVgLWm3/r/jfuQPkvvM16s7XGGz/r1wH/XU9xhtG1BmF+ZYZAG8/b56r8hx1atahZs2a1KpVi+DgYKe9dyFEtuT8IiM90dHRnDhxguPHj3PmzBmuXLnCtfBrXL5+maioKOJi4rBYLCTGJWIxW/Dy9cLoYcTL2wtfP1/y5c1HkfxFKFyoMIUKFaJMmTKULVuWUqVK4eGR/jXqFy1aRKdOnfj2228ZNGiQk9+x60tJSeHUqVOEhYVx4sQJrl27xuUrl7kSfoUb4TdIiE8gKTEJc7KZ5IRkDEYD3v7eGAwG/AL8yJ07N0UKFKFgvoIULlyYkiVLUq5cOcqUKUNgYKDWb8/lhIWFUatWLUJDQ5k/fz56ffqH9C5evMiJEycICwvj3LlzhIeHc/HaRa7duEZcXBxxMXGgIC46DgC/QD/QgX8uf3x9fSlUoBBPFHyCvHnzUqJECcqWLUuZMmUoWrSoM9+uECJncI0iQyujR49m5MiRLF26lJdeeknrOG5JKYVer2fhwoW0b99e6zhuKSIigpo1axIYGMiWLVvw8fHROpIQQtjDAmcP83MpH3zwARcvXqRr165s376dSpUqaR1J5DBJSUm0bNmS1NRUVq5cKQWGECJbsccwS7f23XffUb16dZo1a8bly5e1jiNyEKUU/fr148iRI6xYsYL8+fNrHUkIIewqxxcZJpOJJUuWkDt3blq1akV8fLzWkUQO8dFHH7FgwQKWLFlCxYoVtY4jhBB2l+OLDIBcuXKxcuVKLl++TMeOHbFYLFpHEtnc/PnzGTNmDBMnTiQ0NFTrOEII4RBSZNxRvHhxfv31VzZt2sR7772ndRyRjW3fvp1evXoxfPhw+vfvr3UcIYRwGCky/qV27drMnj2br776ikmTJmkdR2RDZ86coW3btrz00kt8+umnWscRQgiHytGzS9LTvn17wsLCGDJkCE8++SQtWrTQOpLIJiIiImjatCnFihXjp59+euBaGEIIkV3Ip1w6PvzwQ7p27UqXLl04dOiQ1nFENpCSkkK7du0wm82sWrVKpqoKIXIEKTLSodPpmDZtGtWqVaNZs2ZcuXJF60jCjSml6NOnDwcOHJCpqkKIHEWKjAcwmUwsWrQIPz8/mdoqsuTjjz9mwYIFLF68mKefflrrOEII4TRSZDxEUFAQa9as4dKlSzK1VWTK/PnzGT16NN9++y2NGjXSOo4QQjiVFBmPUKJECZYsWcLGjRsZPny41nGEG0mbqvree+8xYMAAreMIIYTTSZHxGJ5//nlmz57N+PHj+f7777WOI9zAmTNnePnll2nRogWjR4/WOo4QQmhCprA+pg4dOnD8+HHeeOMNnnjiCZnaKh4obarqk08+yezZs2WqqhAix5JPvwz4+OOP6dKli0xtFQ+UkpJC+/btZaqqEEIgRUaG6HQ6pk+fTtWqVWVqq7iPUoq+ffuyf/9+maoqhBBIkZFhJpOJxYsX4+vrK1NbxT1GjhzJ/PnzZaqqEELcIUVGJgQFBbF27VouXrxIjx49sFqtWkcSGps/fz6ffvopEyZMkKmqQghxhxQZmVSiRAkWL17M6tWr5aqtOdyWLVvo2bMn77zzDgMHDtQ6jhBCuAwpMrLghRde4KeffmL8+PFMnDhR6zhCA8ePH6dt27a0atWKzz77TOs4QgjhUmQKaxZ17NiR06dP8+abb1K0aFFat26tdSThJNevX6dp06Y89dRTzJo1S6aqCiHEf0iRYQcffPABV69epUuXLmzevJmQkBCtIwkHS0hIoHXr1hiNRpmqKoQQDyB/etnJt99+S2hoKC1btuT06dNaxxEOZLFY6NKlC2fOnGHt2rXkzZtX60hCCOGSpMiwE4PBwNy5cylSpAgvvfQSkZGRTs8wZ84cdDrd3bufn999rzl48CDNmzcnMDAQf39/QkND+fPPP+973fDhw+9pyxFHZx4nL8CaNWsoXbo0RuODD7w5I2+aoUOHsn79elasWMFTTz2Voe992HuOiorihx9+oEGDBgQFBeHt7c1TTz1F165d+fvvv+9ry5nvWQghMkOKDDvy9/dn9erVdw+lJycna5Jj8uTJKKWIi4u75/Hdu3dTq1Yt/P39OX78OOfOnaNEiRLUq1eP9evX3/PasWPHopRCKYXBYNAk75kzZ2jZsiUjRozgxo0bD23DWXk///xzvv/+e+bMmUPNmjUz3U567/mdd97h9ddfp1WrVhw7doyIiAhmzJjBwYMHqVKlCsuWLbunDWfuIyGEyAwpMuysYMGCrF27lsOHD9OzZ0+XWUPDarXSp08fAgMDmTlzJgULFiQ4OJjJkydTsmRJ+vbtq1lR9CAfffQRtWrVYv/+/fj7+2sdh0WLFvH+++/z1Vdf0bZtW4f08corrzBkyBAKFCiAj48PderUYd68eVgsFt59912H9CmEEI4iRYYDlC9fnqVLl7Js2TI+/PBDreMAsG3bNo4ePUq7du3w9va++7jBYKBz585cunSJVatWaZjwftOnT2f48OEPPU3iLNu3b6dHjx4MGTKEIUOGOKSPadOm8eOPP973eKVKlfD29ubMmTMopRzStxBCOIIUGQ5Sr149Zs6cydixY5k0aZLWcdi8eTMAVatWve+5tMc2bdrk1EyP8u9iSEtpl20PDQ1l3LhxTu8/Pj6exMREKlasiE6nc3r/QgiRWVJkOFDnzp35+OOPGTJkCCtWrMhUG2FhYbRu3ZpcuXLh4+ND9erVWbVqFaGhoXcH/PXt2/ex2gEoUqTIfc8VLlwYgJMnT2YqoyPyuopbt27RtGlTihUrxvz589Md++Do97xo0SLANlVaCCHcifbHobO5kSNHcuHCBbp27cq2bduoXLnyY3/v6dOnqVmzJr6+vixevJiaNWty4cIFhg4dyqFDh/D09CQpKemx2oqOjgbA19f3vufSZjhERUU9djZH53UFiYmJtGzZEovFwqpVq9Lddo5+zzdu3GD48OH07duXDh06ZOXtCCGE08mRDAfT6XRMmTKFkJAQmjdvzoULFx77e99//32io6PvXnTLz8+PChUqMG/ePLte/TXtPH9WD8U7K68zWCwWOnXqxKlTp1i3bh358uVL93WOfM8RERE0adKEevXq8cMPP2SpLSGE0IIUGU5gMpn49ddfKVCgAKGhody8efOxvm/dunUANG7c+J7H8+bNS9myZTOUITAwECDdX3xpj6W9JrPsmVdrQ4cOZcOGDSxbtuyha2E46j3Hx8fTuHFjypcvz9y5c2WKqhDCLUmR4SRpa2ikpqbSokWLR/6Vm5ycTGxsLF5eXukuUpU7d+4M9Z/2C+/y5cv3PXflyhUASpcunaE2/81sNts1r5b+7//+j8mTJzNnzhxq1679wNfZex+lSU1NpX379hQuXJiffvpJCgwhhNuSIsOJChYsyJo1azh9+jSdOnUiNTX1ga/19PTE39+fpKSk+xapAggPD89Q3/Xr1wdg//799z2X9ljDhg0z1Oa/mUwmu+bVyty5cxk5ciRff/31I9fCsPc+StO/f3+Sk5NZuHDhPdN3S5Uqxa5duzLVphBCaEGKDCcrV64ca9asYfPmzQwaNOihr23atCnwzyH5NNevX8/wTJC6detSvnx5Fi9efM9ARIvFwvz58ylatCjNmzfPUJuOzKuFzZs388orr/D+++/z+uuvP9b32Ps9f/LJJxw9epTly5fj6emZ4e8XQghXIkWGBmrUqMH8+fOZPn06n3322QNfN2bMGIKCgu6OD4iLi+PIkSP07t2bAgUKZKhPvV7P9OnTiYyMpHfv3ly/fp2IiAgGDRrEqVOnmDp1Kl5eXll6X/bM62z79++nVatWtGvXjv/7v/977O+z53ueNWsWo0aNYvfu3fj7+99zXRKdTseZM2cy+raEEEJTUmRo5KWXXmLSpEl88MEHzJo1K93XlCxZkp07d1KtWjXatWtH/vz56d+/PyNGjKBUqVIZ7jMkJIQdO3Zw+/ZtypQpQ7FixTh16hRbtmy5b+BiZtg776pVq+7+gr1y5QoWi+Xuv6dNm5blvGnOnTtHixYtqF69OjNnzszQLBt7vufFixdnNLoQQrg0WSdDQ/379+f8+fO8+uqrFCxYMN1f9KVLl2bp0qV267Ny5cqsWbPGbu39lz3ztmjRwuHLaEdERNC0aVMKFSrE8uXL8fDwyHAb9nrPrrasuxBCZJUUGRobM2YM169fp3379mzZsoXnnntO60g5RtpiWykpKWzZsuWBl5oXQgiROXK6RGNpi3XVqlWLJk2acOrUqSy3OXDgQHQ6XZZ+aQ4fPvzuqQmLxZLlTA+jRV6LxULXrl05ceIEa9eudfqYEXfbR0IIkSlKuISYmBj13HPPqZIlS6obN2488HW//PKLAu659+nTx4lJ72W1WhWgFi5cmO7zrpY3zaBBg5S3t7f6888/7d62q75nIYRwsvk6peTa0a7i2rVr1KpVi4IFC7Jx40Z8fHy0jvRISin0ej0LFy6kffv2Wsd5LP/3f//HqFGj+PXXX2nZsqXWcYQQIrtaIKdLXEjBggVZt24dJ0+epEOHDpjNZq0jZTtTpkxh5MiRfPfdd1JgCCGEg0mR4WLKlCnD2rVr2bp1K7169cJqtWodKdtYtmwZgwYN4pNPPmHAgAFaxxFCiGxPigwXVK1aNZYvX86SJUsee+VJ8XCbN2+mU6dOvPrqq3z88cdaxxFCiBxBigwX1aBBA+bPn8+PP/6YoRUoxf327t1L69atadu2LRMnTtQ6jhBC5BiyToYLa926NZMmTWLAgAEEBAQwZMgQrSO5nVOnTtGiRQtq1KjBzJkz0eulrhZCCGeRIsPF9e/fn5s3bzJs2DAKFChAx44dtY7kNi5fvkyjRo0oXrw4y5YtkwuOCSGEk0mR4QY+/PBDoqKi6N69OwEBAXev/Cke7NatW7z44ov4+/uzZs0afH19tY4khBA5jhQZbuLLL78kKiqKdu3asX79emrXrq11JJeVkJBAy5YtiYuL488//yQoKEjrSEIIkSNJkeEmdDodU6dO5fbt27Ro0YItW7ZQqVIlrWO5nJSUFNq2bcvp06fZvn07RYsW1TqSEELkWDIKzo0YDAbmzJnD008/TfPmzTl//rzWkVyKxWKhW7du7Ny5k3Xr1lGmTBmtIwkhRI4my4q7odu3b1O/fn1iY2P5448/yJ8/v9P6Dg0NZc+ePfdcgj0pKQkPD497Zm6YTCYOHTpEkSJFnJJLKUX//v2ZM2cO69at44UXXnBKv0IIIR5IlhV3R7ly5WLdunXo9XpefPFFoqOjndZ306ZNiY2NJS4u7u49NTWVhISEu/+Oj4+nZMmSTiswAN5//31mzJjBnDlzpMAQQggXIUWGm8qXLx9r167l5s2btGnThqSkJKf027lz50euNWEwGOjZs6dT8gB89913fP755/z444+0bdvWaf0KIYR4OCky3FiJEiX47bff+Pvvv+nYsSOpqanpvu7q1at267NQoULUqlXroYWG1Wq16xVZb968+cCLxc2ZM4chQ4bwxRdf0KdPH7v1KYQQIuukyHBzTz/9NGvWrGHTpk288sor/HeIzeTJkwkJCbHrFV27d++OTqdL9zmDwUD9+vXtOk5k1KhRtG7d+r6jNStXrqR379689957vP3223brTwghhH1IkZENhISEMH/+fObPn8/w4cPvPv75558zaNAgLl++zKJFi+zWX/v27R9YZCil6N69u936ioyMZPr06axZs4bGjRsTFxcHwM6dO+nUqRNdu3Zl9OjRdutPCCGE/cjskmxk7ty59OjRg7Fjx2KxWBgxYgQAer2eChUqcOjQIbv11axZM9avX4/FYrnncZPJxM2bN8mVK5dd+hkzZgwjR44kNTUVk8lEhQoV+Pbbb2nVqhUvvPACixcvxmiU5V6EEMIFLZAiI5v5+uuv+b//+z+io6PvO3WyZcsW6tata5d+5s2bR7du3e7pw2g08tJLL/Hrr7/apQ+z2UzRokW5cePG3cdMJhO+vr5UqFCBTZs2yfVIhBDCdckU1uzEYrFw+PBhbt++fV+BYTQaGTdunN36at269X2/4NMWw7KXefPmcfPmzXseM5vNxMfHc/78ea5cuWK3voQQQtifHMnIJlJSUujcuTPLly+/7xRGGp1Ox7Fjxyhbtqxd+uzUqRO//vrr3UGlPj4+3Lp1C29vb7u0X7FiRY4fP47Var3vOaPRSN68edmyZQulS5e2S39CCCHsSo5kZAcJCQk0b96cFStWPLDAANsv5m+//dZu/Xbt2vVugWEymWjXrp3dCozNmzdz9OjRdAsMgNTUVG7evEmtWrXsOtZECCGE/UiRkQ3s3r2bY8eO3XeK5L/MZjMzZswgIiLCLv02adKEgICAu2137drVLu0CjBs37pEDOnU6HbGxsSxbtsxu/QohhLAfKTKygfr163Pu3Dm+//578ubN+9BfzlarlR9//NEu/ZpMJjp16gRA7ty5adCggV3aPXHiBL/99tsDFxczmUwYDAY6duxIWFgYH3/8sV36FUIIYV9SZGQTHh4evPrqq5w/f54vv/ySoKAgDAbDfa8zm82MHz8+08uQR0bC0aPw55+wcSMUK9YZgJCQrmzZYuT33+Gvv+DqVXjImZuH+vLLL9MtlEwmEzqdjpYtW3L8+HF+/vlnihcvnrlOhBBCOJwM/Mym4uLimDRpEp9++inJycn3rPip1+uZMWPGA68vopStkDhwwPb18GEIO6G4elVH8n21iRV4AlgI1LrnGYMB8uZTlCyp4+mKULEiPPMMVKsGXl7p57558yZFihQhJSXl7mNGoxGLxUKTJk347LPPqFSpUoa3hxBCCKeTdTKyu8jISL799lvGjRtHSkoKqamp6PV6SpcuzbFjx+6u3HnhAqxYAZt/h23bIDICTB7wRKlUCpc0U6RkKnkKWMidz0qefBZ8c1kxeYLJQ7Fx0TQad3qFpEQDygpxt/VE39ITecNA1E09V88ZuXLGyIWTJmJv6/DwhOrVoH59aNYMatSAtAVER40axaeffnp38S2z2Uy9evX48ssvqVKlioZbUgghRAZJkZFTXL9+nTFjxvDDDz+QmpqKUopfftnI2bMNWbxE8dcBHb7+igrVUihfPZny1VIoVtZMOmdc7qOUeuAy4/8VfsXAsb0eHN3rwfG9nly7aKBgIUXbNjo6dEiiTZvCREZGAvDCCy8wduxYatasmZW3LoQQQhtSZOQ0Fy5cZMCA//Hbb7OAxuQKWkm1hkmEvJhExRopGE3O/XG4cNLIng1e7FrvzfkTs4E+PPlkNSZNGkvz5vYZSCqEEEITUmTkJBs3wvARsH8fPFn6NL65xtDngz4UK1tC62gAfNrvdayprTn5dxd8vPUMHgxvvgl2ugyKEEII55IiIyfYvRteGwR/HYAaoUm0ey2O4uVsA0GtFgv6xzkn4mDKagWdDp1OR0yUnpUzfVk3zxdvTx1jxkDfvqCXuVBCCOFOpMjIzqKiYPhwmDYNKlZPoefwGIqVNT/6G11EbLSeJT/6seZnX56rDD/+CJUra51KCCHEY5IiI7v64w/o3AUSkqz0eC+GOi0StY6UaRdOGpn+v1ycPOTBmNHw1lv/zEYRQgjhsqTIyI7GjoWPPoLKLyTz2uhoAnKnf/0Pd6KssGy6H/O/9Se0IfzyCwQGap1KCCHEQ0iRkZ1YLPDaazBtOvR6L4Zm3eOz3V/8p/428eWQIPIH6/htnY7ChbVOJIQQ4gGkyMguUlOhfXtY95viza+iqVo/c8uGu4Nb1wyMeTUIS5KRrVughGtMjhFCCHEvudR7dqAUvNIHftug+HhGZLYuMACCC1r439wIvHKZafSi4sYNrRMJIYRIjxQZ2cCHH8L8X+Cdb6MoUznl0d+QDfgFWHn/x0iSLVaaNoNMXu9NCCGEA0mR4eY2brQN9Ow38jaVaidrHcepcuWx8sHUCE6dVrzzjtZphBBC/JcUGW4sKgp69IBaTZJo2C5B6ziayF/Uwquf3GbSJFizRus0Qggh/k2KDDf26aeQmGLl1U9uax1FU7WbJVK7WSJDhirM7rPWmBBCZHtSZLips2fhu0nQYXAsvgHuvw5GVnUdFsvFCzqmTNE6iRBCiDRSZLipiRMhT34LoR1y5mmS/8pX2EJoh3i+HK+QSdlCCOEapMhwQykp8PMcqN82ARe4tpnLaNIlgfPndGzZonUSIYQQIEWGW9q0CSIjoF5r970eiSMULpFK6Upm5s3TOokQQggAo9YBRMZt3w5FS1rIU8Bi13Zjo6NY8sME9mz6jYjrVwkIykPhEqWo2/JlajdthYeXV4Ze+0GXVoQd2Hv3e154qS1Dxn3HJ707cHjnH3cfn70nDN+AALu8h6dDktm2xQhks/XUhRDCDUmR4YZ27FSUqmTfNTGib4UzolNLUpKSGPjpOCpUq0lyUiIbF87luxFvEh8TQ4ue/TL02tHzlnM+7Bjvd25JwSeLMeB/XwDwwY9z+Kh7W1r06MfzzVvZ9X2UqZzCr1P8iIyEoCC7Ni2EECKD5HSJGzp/HgoXt+9RjDnjPyP88kX6fPA/qtZrhLevH4F58tJu4FAq16mf6dcWK1uewZ99zfmwY3z77hsopfjh43d5JuR5uxcYAIWKW1AKLl60e9NCCCEySI5kuKHICB3+gfadtrp741oAKr/Q4L7nPpw6N9OvBajV5CXODzjGkh8m8EGXVvjnys3gMV/ZI/Z90rbLrVsOaV4IIUQGyJEMN5SYCB5e9punaU5JISE2BpOnJ96+fnZ77b91HvIuT1V6jhN/7aNmkxbo9I750fP0tm2X+HiHNC+EECIDpMhwQ7kCIe62/QY2mjw88PEPwJycTGJ8nN1e+29H9+wgITaGJ0uXY8qoEZwPO5bV2OlK2y4yHkMIIbQnRYYbCg5WxETZd4GMGqFNATiwdfN9z73dphEzPxuZqdcChF++yKQP3uLdb6cxfPIsPL28GPtaL2IiI+z5FgCIibRtl+BguzcthBAig6TIcEMVK+o4f9xk1za7vTWCfEWeYOZnI9m/ZSOJ8XFEXL/GlFEjiAoP56Ver2bqtUkJ8Ywd1JtX3v8fRUqVJl/horz97VQiw28wbkg/LKn2vdjI2WNGPL2gZEm7NiuEECITdErJIszu5uuv4X+jrUz74wY6Oy4HERsdxeLJ39xZ++IaAbmDqFA9hE5vvEvBJ4tn+LXT/u8D1s6d+U/ulZsJDM5H75oV72mr85B3aTdwqF3eww8f5yL+ig9//vHo1wohhHCoBVJkuKFDh6BSJRg9L4Kyz6VoHcdlWCwwsEF+Bg/Q88knWqcRQogcb4GcLnFDzzwDlZ5VbF7irXUUl3JgqxeR4Xq6d9c6iRBCCJAxGW6rX18df67xJuqm7MI0q2f70qCBjMcQQghXIb+h3FSfPpA3r44FE/21juISDmz15PAuDzlNIoQQLkSKDDfl5QWjP4VNS3w4fdi+M03cTUqSjtnjAmjVGp5/Xus0Qggh0kiR4ca6doUG9eGbt3KTEJdzrzo6a2wAMTeNfPO11kmEEEL8mxQZbkyvh9mzwZxg4IePAlH2vZyJW9i20pv1C3yYNg2KFdM6jRBCiH+TIsPNFSwICxfC3s1ezPwsQOs4TnXwD0++fz+Qt9+Gdu20TiOEEOK/pMjIBurXh7lzYN08X+aM9ycnrHxyaIcn497ITafO8PnnWqcRQgiRHikysol27WDmTFg5y49JIwKxpGqdyHG2rfBmTP8g2rbRMX0adl31VAghhP0YtQ4g7Kd7d8ifH15+2Zsbl40MGRdFcEGL1rHsxpIKc78KYMVMX95+23YEQwoMIYRwXXIkI5t58UXYuRNUvIl32uRl1wYvrSPZxfWLRj7sEsyGBb7MnAlffCEFhhBCuDopMrKhihVh3z7o0E7HuNdz8/mgIMKv2PfS8M5iTtax4Dt/3nwpGC+dkf37oGdPrVMJIYR4HHKBtGxu82Z4bRBcuKBo1j2elq/E4x/o+nNdLRbYvtKbxd/7Extp4JNP4I03wJSz1x0TQgh3IldhzQlSUmDiRBg7FhKSFI27xNO0SwJ5CrjeeI2UJB1/rPZm6RQ/rl/6klatmvHdd09TqJDWyYQQQmSQFBk5SXw8fP89jPsSbt1aTbX6oYS2j+fZ55MxaDwE+OIpIxsX+bBtuQ9JiTq6dDYTFtaIc+eO8/vvv1O+fHltAwohhMgoKTJyouHDP2DcuM957rl97N//LP65FFXrJ1HjxSQq1kjGy8fxPxJWC5w9ZmLPRi/2bPTm0hkDxUso+r+qo3dvyJcPEhISaNasGcePS6EhhBBuSIqMnGbChAm8+eabTJ06lT59+nDuHCxZAkt+hT27QaeHkhXMlKuSQqlnUnjiqVQKFkvFkMVxo5E3DFw6beTMURNh+z0I2+9BfJyOJ55UtHtZR9u2ULOmban0f4uPj6dp06acPXuWrVu3UlKu4y6EEO5CioycZObMmfTp04cvv/ySYcOG3ff8jRuwdSts2wa/b1GcCNNhsdgGWxYunkqeghZyBVvIU8CKr58VL1+FwQBevlZSU3QkJ+kwp+hIiNURfUtP5A0D0TcNXD1vJPa2bb5pwUKKF+roeOEFqFsXKlR4dO7bt2/z4osvcu3aNbZs2UKJEiXsvWmEEELYnxQZOcWSJUvo2LEjo0aN4oMPPnis70lKguPH4cgROHECLl+Ga9fg0mVFbCzExeowmyE+fgpGUyh+viXw9FL4+0P+/DqKFoECBeCpp2zFxNNPQ1BQ5vJHR0fTqFEjwsPD2bp1K8XkamhCCOHqpMjICVauXMnLL7/Ma6+9xjfffGPXtpVS6PV6Fi5cSPv27e3a9n/dunWLhg0bkpCQwLZt2yhYsKBD+xNCCJElC2Qxrmzu999/p0OHDnTt2pWvv/5a6zhZEhwczIYNGzAYDDRr1oyYmBitIwkhhHgIKTKysT179tCqVSuaNWvGtGnT0GWDdbjz5cvHxo0buXXrFq1atSI5OVnrSEIIIR5Aioxs6siRIzRt2pSaNWsyb948DFmdHuJCihQpwsqVKzlw4AC9e/fGanX9FUyFECInkiIjGzpz5gwvvvgilSpVYtmyZXh6emodye6effZZfv31V5YsWcJHH32kdRwhhBDpkCIjm7ly5QqNGjWiaNGiLF++HG9vb60jOUzDhg2ZMmUKn332GXPmzNE6jhBCiP/QeDFpYU83b96kUaNG+Pr6smbNGvz9/bWO5HA9e/bk8OHD9OvXj1KlShESEqJ1JCGEEHfIkYxs4vbt2zRt2pSUlBTWr19Pnjx5tI7kNJ9//jkNGzakTZs2XL58Wes4Qggh7pAiIxuIj4+nWbNm3Lhxg40bN+a49SMMBgO//PILuXPnpmPHjpjNZq0jCSGEQIoMt2c2m+nQoQOnTp1iw4YNOXYlTH9/f5YuXcqRI0d45513tI4jhBACKTLcmlKK/v37s2XLFpYvX07ZsmW1jqSpMmXKMGXKFCZMmCADQYUQwgVIkeHG3nvvPebMmcOSJUuoWbOm1nFcQseOHRk0aBCvvfYaZ86c0TqOEELkaFJkuKlJkybx5ZdfMmXKFJo0aaJ1HJcyfvx4SpQoQffu3bFYLFrHEUKIHEuKDDc0f/583njjDcaNG0evXr20juNyPD09mTdvHgcPHuSzzz7TOo4QQuRYUmS4mc2bN9OrVy/eeust3nrrLa3juKzy5cszevRoRo0axe7du7WOI4QQOZIUGW5k3759tGrVipdffpmxY8dqHcflDRkyhLp169KnTx9SUlK0jiOEEDmOFBlu4syZM7Ro0YIaNWowc+ZM9HrZdY+i1+uZPn0658+fZ9y4cVrHEUKIHEd+U7mBmzdv0rRpU5544gmWLVuGh4eH1pHcxpNPPslHH33E//3f/3HixAmt4wghRI4iRYaLi4mJoXHjxiilWLlyJX5+flpHcjtvvfUW5cqVY8CAASiltI4jhBA5hhQZLiwlJYV27dpx48YNNmzYQP78+bWO5JaMRiOTJ09m69at/Prrr1rHEUKIHEOKDBdltVrp1q0bu3fvZvXq1Tl2uXB7CQkJoXPnzrz77rsyCFQIIZxEigwXNWzYMJYvX86SJUt49tlntY6TLYwePZorV64wdepUraMIIUSOIEWGCxo9ejTfffcd8+bNIzQ0VOs42UaxYsUYPHgwo0aNIiYmRus4QgiR7UmR4WLmzJnDRx99xMSJE3n55Ze1jpPtvP/++5jNZiZMmKB1FCGEyPakyHAhf/zxB3379uWdd95h4MCBGf7+OXPmoNPp7t7/PRMlKiqKH374gQYNGhAUFIS3tzdPPfUUXbt25e+//76vreHDh9/TVkhISJbeW0bzpjl48CDNmzcnMDAQf39/QkND+fPPPzOdNygoiCFDhvDVV18RHR1t77ckhBDiX6TIcBFnz56lbdu2NG/ePMvX25g8eTJKKeLi4u4+9s477/D666/TqlUrjh07RkREBDNmzODgwYNUqVKFZcuW3dPG2LFjUUqhlMJgMGQpT2byAuzevZtatWrh7+/P8ePHOXfuHCVKlKBevXqsX78+03nffPNNdDod3333nd3fixBCiH9IkeECIiMj7y62NXv2bIet5vnKK68wZMgQChQogI+PD3Xq1GHevHlYLBbeffddh/SZWVarlT59+hAYGMjMmTMpWLAgwcHBTJ48mZIlS9K3b1+Sk5Mz1XauXLl4/fXXGT9+vBzNEEIIB5IiQ2Npa2EkJyezatUqfH19HdLPtGnT+PHHH+97vFKlSnh7e3PmzBmXWqhq27ZtHD16lHbt2uHt7X33cYPBQOfOnbl06RKrVq3KdPvDhg0D4Pvvv89yViGEEOmTIkNDSin69u3Lvn37WLFiBQUKFHB6hvj4eBITE6lYsSI6nc7p/T/I5s2bAahatep9z6U9tmnTpky3n3Y046uvviI2NjbT7QghhHgwKTI0NGrUKH755RcWL17MM888k+5rwsLCaN26Nbly5cLHx4fq1auzatUqQkND7w5y7Nu3b6YzLFq0CIAPPvgg0204Im9YWBgARYoUue+5woULA3Dy5MksZR02bBgWi0XGZgghhIMYtQ6QUy1YsID//e9/TJo0iRdffDHd15w+fZqaNWvi6+vL4sWLqVmzJhcuXGDo0KEcOnQIT09PkpKSMp3hxo0bDB8+nL59+9KhQ4dMt+OIvGljJdI7fZQ2CyUqKipLeQMDAxk8eDDjx49n8ODB+Pv7Z6k9IYQQ95IjGRrYvn07PXv2fORU1ffff5/o6GgmTJhAo0aN8PPzo0KFCsybN4/4+PgsZYiIiKBJkybUq1ePH374IUttOSPvv6WNHbHH6Z233noLi8XCpEmTstyWEEKIe0mR4WRnz57l5ZdffqypquvWrQOgcePG9zyeN29eypYtm+kM8fHxNG7cmPLlyzN37ly7TVG1Z97AwMC7Wf8r7bG012RFYGAggwYNYvz48fdNoRVCCJE1UmQ4UURExGNPVU1OTiY2NhYvL690F6nKnTt3pjKkpqbSvn17ChcuzE8//WS3AsNsNts1b1pRcvny5fueu3LlCgClS5fORNL7DRs2jOTkZLsd0RFCCGEjRYaTpKSk0L59+8eequrp6Ym/vz9JSUnp/oUdHh6eqRz9+/cnOTmZhQsXYjT+MySnVKlS7Nq1K1NtAphMJrvmrV+/PgD79++/77m0xxo2bJiJpPcLCgqif//+jB8/PktjXIQQQtxLigwnyOxU1aZNmwL/nIZIc/369UzNrPjkk084evQoy5cvx9PTM8Pf/yj2zFu3bl3Kly/P4sWL7/nFb7FYmD9/PkWLFqV58+ZZD33HW2+9RXR0NLNnz7Zbm0IIkdNJkeEEjzNVNT1jxowhKCiIoUOHsmHDBuLi4jhy5Ai9e/fO8Joas2bNYtSoUezevRt/f/97rvOh0+k4c+ZMRt+WQ/Pq9XqmT59OZGQkvXv35vr160RERDBo0CBOnTrF1KlT8fLyynLmNAUKFKBnz558/vnnpKam2q1dIYTIyaTIcLC0qarffvvtA6eqPkjJkiXZuXMn1apVo127duTPn5/+/fszYsQISpUqlaG2Fi9enKHXZ4Y98wKEhISwY8cObt++TZkyZShWrBinTp1iy5Yt9w0utYd3332Xixcv3l07RAghRNbIOhkOlDZV9e23387UVVXBNrhx6dKlWc6SlSW4M8JeedNUrlyZNWvW2K29hylRogQdOnTg008/pWPHjg67howQQuQU8inqIP+eqjp27Fit44jH9MEHHxAWFsbq1au1jiKEEG5PigwHiIyMpEmTJhQvXpyff/7Z6X8RDxw4EJ1Ol+5U0sc1fPjwu+M1LBaLHdPdz5Xyli9fnubNmzN69OhMtyGEEMJGp1zp0pvZgNlspkmTJpw+fZrdu3fb/aJn8+fPp3Pnzvc81qdPH6ZNm2bXfh6XUgq9Xs/ChQtp3779fc+7Wt7HsXv3bkJCQvj999+pV6+e1nGEEMJdLZAiw84GDRrEzJkz2bZtW7pXEM1uHlVkuKv69evj7e3ttPEgQgiRDS2Q0yV2NGPGDCZPnszMmTNzRIGRnQ0bNox169Zx7NgxraMIIYTbkiLDTrZv387AgQMZOXIkHTt21DqOyKIWLVpQtmxZvvnmG62jCCGE25Iiww7Onz/Pyy+/TIsWLfj444+1jiPsQKfT8frrrzN79mxu3LihdRwhhHBLUmRkUWxsLC1btqRw4cLMnj3bLpcfF66hZ8+e+Pv78+OPP2odRQgh3JIUGVlgtVrp1q0b4eHhrFix4pEXPRPuxcfHh/79+zNp0iS5cJoQQmSCFBlZ8P7777N27VoWLlxI0aJFtY4jHOD1118nJiaGefPmaR1FCCHcjhQZmbRo0SK++OILvv/+e1544QWt4wgHyZ8/Px06dODrr79GZnsLIUTGSJGRCQcOHKBXr1689dZb9O3bV+s4wsHefPNNjhw5wsaNG7WOIoQQbkWKjAy6du0arVq1ok6dOnJNkhzi2WefpW7dukyaNEnrKEII4VakyMiApKQkWrduja+vL/Pnz8dgMGgdSTjJgAEDWLVqFRcvXtQ6ihBCuA0pMh6TUoo+ffpw6tQpVq5cSWBgoNaRNBEaGkpAQAD+/v74+/sTEBCA0WikV69edx/z9/cnKCiIy5cvax3Xbtq2bUtwcDAzZszQOooQQrgNKTL+JTU19YHPjRkzhgULFjB37lyeeuopJ6ZyLU2bNiU2Npa4uLi799TUVBISEu7+Oz4+npIlS1KkSBGt49qNh4cHvXv3ZsqUKZjNZq3jCCGEW5Ai41+mTp3KwIEDSUlJuefx5cuX8/HHH/PNN9/QtGlTjdK5hs6dOz/y0vUGg4GePXs6KZHzDBw4kPDwcFatWqV1FCGEcAtyFdZ/qVGjBnv27KFGjRosX76c/Pnzc+zYMWrWrMnLL78sh8rvqFOnDjt27MBqtab7vF6v5+rVq+TPn9/JyRyvWbNmWCwWfvvtN62jCCGEq5OrsKY5d+4ce/fuBWxTVCtVqsSmTZto2bIlzzzzDD/88IPGCV1H9+7dH7h8usFgoH79+tmywADbANANGzZw6tQpraMIIYTLkyLjjrlz52I0GgEwm81ERETQpEkT9Ho9S5cuxcPDQ+OErqN9+/YPLDKUUnTv3t3JiZynefPmFC1alKlTp2odRQghXJ4UGXfMnj37ngF9qamppKamcurUKd5//30Z7PcvuXPnplGjRulO4TUYDLRu3dr5oZzEYDDwyiuv8PPPPz90oLAQQggpMgDYv3//Qw9/z5gxg3r16hEeHu7EVK6tW7du943JMBqNtGjRgly5cmmUyjm6devGjRs32LRpk9ZRhBDCpUmRAcybN++hp0MsFgu7du2iWrVqHDt2zInJXFfr1q3x9PS85zGLxUK3bt00SuQ8JUuWJCQkhDlz5mgdRQghXFqOLzIsFguzZ8++b9rqv6WN1Wjfvj1PPvmks6K5NB8fH1q1aoXJZLr7mLe3d46Z4tutWzeWLl1KXFyc1lGEEMJl5fgiY/Pmzdy6deuBz+v1eipUqMDu3bv58ssv8fX1dWI619a1a9e7Y1VMJhPt2rXD29tb41TO0alTJ8xmM0uXLtU6ihBCuKwcX2TMmTMn3VMlRqMRPz8/vvrqKw4cOEDVqlU1SOfamjRpQkBAAGCbkdO1a1eNEzlPUFAQTZo0Ye7cuVpHEUIIl5Wji4ykpCSWLFlyz6mStBkTrVq14vTp0wwZMuSRK1zmVCaTiU6dOgG2GScNGjTQOJFzdevWjQ0bNnDlyhWtowghhEsyah3gcYSHhxMeHs7t27dJTEwkNTWV2NhYAAICAjAYDPj4+BAQEEC+fPnIly/fY7W7fPlyEhIS7v7bYDBQuHBhpk6dyosvvuiQ9+LuIiPh2jWIjobERChWrDMwhZCQrmzZYsRggMBAyJ/fds/OF6p96aWXCAgIYMGCBQwbNkzrOEII4XJcZlnxq1evsm/fPsLCwjhx4gRhhw5x/vx5wqOiSLVYMtSWyWAgX1AQTxYrRrlnnqFMmTKULVuWqlWrUrBgwbuva9GiBWvXrsVgMKCU4oMPPmD48OF4eXnZ++25FaXg6FE4cMD29fBhCDuhuHpVR3LSf19tBZ4AFgK17nnGYIC8+RQlS+p4uiJUrAjPPAPVqkF22cQ9e/bkzJkz/PHHH1pHEUIIV7NAsyLj7NmzrF27lj//+IMdW7dy4do1AIp6eFDGaqVsaiolgXxA4TtfAwEvwAAE3GknBrAASUAUEA5cvfP1NHDCaCRMr+fynVMixQoVolbdulR+7jmGDx+OxWLhhRdeYOrUqZQuXdpJ7971XLgAK1bA5t9h2zaIjACTBzxRKpXCJc0UKZlKngIWcuezkiefBd9cVkyeYPJQbFw0jcadXiEp0YCyQtxtPdG39ETeMBB1U8/Vc0aunDFy4aSJ2Ns6PDyhejWoXx+aNYMaNeABC4i6vKVLl9KuXTuuXLlCgQIFtI4jhBCuxLlFxu7du1m0aBGrly4l7OxZAoxGaitFTYuFWkA1/ike7C0G2A3sBHYZDPyuFElWK4Xz56dz9+506NCBatWqOah313T5MsyeDYuXKP46oMPXX1GhWgrlqydTvloKxcqaH+t0h1LqgcuM/1f4FQPH9npwdK8Hx/d6cu2igYKFFG3b6OjWDUJCsvimnCwhIYG8efMyceJEXnnlFa3jCCGEK3F8kXHt2jV++uknZk+fzvHTpynj4cFLKSk0A54HTI9qwEGmA8HAdmCFhwenUlIo/9RT9Ozblx49emTbv0qVgg0b4PvJsHoV+OWyUq1hEiEvJlGxRgpGk3MPbF04aWTPBi92rffm/AkjTz+jeG2greDw83NqlEx76aWX0Ov1LF++XOsoQgjhShxXZJw6dYrvvv2WKT/+iJdSdEhNpTu2wsIV7QdmA7+YTNxWio6dOvHe8OFUqFBB62h2s3EjDB8B+/dBqYpmQjskULdlIh5eLjEshzNHTWxc4MP2Vd74eOsYPBjefBNcfZXyadOm8cYbb3Dz5k1ZR0UIIf5h/yIjLCyM4e+8w4rVqylrNPKW2Uw3wPOR3+kakoCfga9MJk6kptKmZUs+++ILtx6vsXs3vDYI/joANUKTaPdaHMXLue4F32Ki9Kyc6cu6eb54e+oYMwb69gVXnUkcHh5OoUKFWLJkCa1atdI6jhBCuIoFdvvYjoiIYPDgwTxTsSJn169nuVIcNZvpg/sUGGAbWNoPOGo2s0wpTq5ZQ8Xy5Xnj9deJjIzUOl6GREVB//5QqxakmlL4cukt3pkY5dIFBkBAbitdh8Xy/cZwarWM57VBtrEaf/2ldbL05cuXjxo1asjpEiGE+A+7FBnLli2jQunS/DplCt9bLPyVksJLgJtOGABsG6YlcNBsZpLFwqIff6RC6dKsWLFC62iP5Y8/4JlKsHiplTe+iGbkrAiKlXXt4uK//AOt9HovhnG/3iRBpVAjBL780jauxNW0bNmSNWvW4CIzwoUQwiVkqciIi4ujW5cutGnThmbR0Rwzm+mLbYppdmHAdmTjuNlM48hIWrVqRc/u3YmPj9c62gONHWubHlrwqWTGr7hJnRaJWkfKkidLpzJqdgQdX49lxPu2aa/R0VqnuleDBg24ceMGx48f1zqKEEK4jEyPyTh//jytmjXj2qlTzEpNpZm9k7moVUBvo5EiZcqwfM0annjiCa0j3WWxwGuvwbTp0Ou9GJp1j3fb9Sce5NTfJr4cEkT+YB2/rdNRuLDWiWwsFgvBwcGMHj2a1157Tes4QgjhCjI3JmPv3r1Uq1wZ/enT7M1BBQZAC2BPaiqWkyep9uyz7N+/X+tIAKSmQrt2MPtnxXvfRdG8R/YrMACeqmRm9C+3iEu2EFITzp7VOpGNwWCgdu3abNmyResoQgjhMjJcZOzdu5cXGzSgemwsf5jNPOmIVC6uOLDDbKZyTAyN6tfnwIEDmuZRCl7pA79tUHw8I5Kq9e9b+ztbCS5o4X9zI/DKZabRi4obN7ROZFOvXj22bNki4zKEEOKODBUZR44c4cUGDaiVmMivFgs5eUUAP2CZxUL1xEQa1a+v6bn4Dz+E+b/AO99GUaZyyqO/IRvwC7Dy/o+RJFusNG0GSS5QV9WrV4+bN29y9OhRraMIIYRLeOwiIzo6mjYtWvBMcjK/WixuNS3VUbyAZamplE1IoE2LFsTExDg9w8aNtoGe/UbeplLtZKf3r6Vceax8MDWCU6cV77yjdRqoXLkygYGBcspECCHueOwio2e3biRevcpCs1kKjH/xAhalphJz6RK9e/Rwat9RUdCjB9RqkkTDdgmP/oZsKH9RC69+cptJk2DNGm2zGAwGnn/+eSkyhBDijscqMhYvXszK1av5xWwmv6MTpWMOtjU30u7/vqRFFPAD0AAIAryBp4CuwN/ptDX8P23Z43pchYB5ZjNLly9n2bJldmjx8Xz6KSSmWHn1k9tO6W/PxnW8XLbQ3bs52TWOnNRulkjtZokMGaowa7wUSEhICPv27cv098+ZMwedTnf37veAC7isWbOG0qVLYzQaH9jW8OHD72krxN2uPieEcHuPLDLi4+MZ9vrr9NLrqeOMRA8xGVBA3L8eewd4HWgFHAMigBnAQaAKsOw/bYy904bCvut51AO66vW8OXgwiYmOX5fi7Fn4bhJ0GByLb4DV4f0BVA9twpKwq1Rv2Ngp/WVE12GxXLygY8oUbXNUrlyZCxcucOvWrSy1M3nyZJRSxMXF3fP4mTNnaNmyJSNGjODGI0a8jh07FqUUSikMj3M5XSGEsLNHFhkzZ84k8uZNxlqd84ssM14BhgAFAB+gDjAPsADvOjHHOKuVG9evM3v2bIf3NXEi5MlvIbRDzjxN8l/5ClsI7RDPl+OVpiuCVq5cGYBDhw45pP2PPvqIWrVqsX//fvz9/R3ShxBC2Msji4wfJk6km9VKPmekyYRpwI/pPF4J26mTM9iOWjhDAaCz1cq348c7dBpjSgr8PAfqt01A/kD9R5MuCZw/p0PLIREFCxYkf/78/OWgC61Mnz6d4cOHP/Q0iRBCuIqHFhm7du3i6MmTDHDDef/xQCJQEedeQ2WgUhw7dcqhi3Rt2gSREVCvtXsvF25vhUukUrqSmXnztM3x7LPPcvDgQYe07e3t7ZB2hRDCER5aZGzevJlCJhPPOqDjMKA1kAvbKY7q2JbsDuWfQZl9s9D+ojtfP8hCG5lRBShgMvH77787rI/t26FoSQt5Clgc1sfjiLoVzvg3B9C9Wll61ijPmAE9uH7xvKaZng5JZtt2bYviypUr33MkIywsjNatW5MrVy58fHyoXr06q1atIjQ09O6gzL59s/LTLoQQrumhRcb233+nfmqq3Ts9DdQE9gGLgXBgJjABOITt0vAK26mQzLiBbRZJX6BDVsNmkA6oa7GwzYFFxo6dilKVtJ/ZMXPMx7To2Y9p2w7w9oQpHNu3m6/f0va6HWUqp3DqpI7ISO0yVK5cmbCwMBISEjh9+jQ1a9Zk3759LF68mPDwcGbOnMmECRM4dOgQnp6eKKWYNi2zP+1CCOG6HlpknDl5kvIOOFXyPhCNrahohG1KagVsgzWzem3TCKAJttkeP2SxrcwqZ7Vy9uRJh7V//jwULq7tUQyAhu27UObZKnh6+/B0yPNUrRfK6cMHiYnS7jd8oeIWlIKLFzWLQMWKFbFYLJw6dYr333+f6OhoJkyYQKNGjfDz86NChQrMmzfPpa/kK4QQ9vDQIuNmZCTBDuh03Z2v/50ImRcom4V24++0WR6Yi3aXnM8HhGdxCuPDREbo8A/UfrZPqaefveffQfkLAhAVrt3FRNK2iwM3/yMVL14cnU7H+fPnWbfO9tPeuPG9P+158+albNms/LQLIYTre2iRkZCUZPfrkyQDsdhWykxvmaHcmWw3FWgPFAZ+QrsCA8AXiHPgWhmJieDhpf1gXF+/gHv+rdfbhthaNZzu7Olt2y5aHiTw9vYmf/78nDp1itjYWLy8vNJdVCt37sz+tAshhHt4aJERFBBAhJ079AT8gSTuXVQrTXgm2+2PrYBZCPx7cl8pYFcm28ysW0BeB/4CyRUIcbez4XXc7SBtuwQFaZujePHiXLp0CX9/f5KSku5bVAsgPDyzP+1CCOEeHlpk5MubN9O/9B+m6Z2v6/7z+HUgMyMZPgGOAsvBJa6rEg7kDXbEiSab4GBFTJQskJGemEjbdnHg5n8sxYoV49y5czRtavtpTzttkub69eucdOC4HSGEcAUPLTKeqVqVPQ5Y9GcMtuuMDAU2YDuicQTojW1Bq4yYBYwCdmM7QqL7z/2MPQJn0B6TiUrVqjms/YoVdZw/bnJY++7s7DEjnl5QsqS2OYoXL865c+cYM2YMQUFBDB06lA0bNhAXF8eRI0fo3bs3BQpk9KddCCHcy0OLjHr167NDKVLs3GlJYCdQDWgH5Md2umMEttMbGbHYvtGyLAnYabVSt149h/VRuxacPGhy+vLZJ//ez8tlC7Fn028AdKpUnHnffA7Ay2ULsXTqJADebtOIMQOce0XaNCf+8qBKFfDw0KT7u9KKjJIlS7Jz506qVatGu3btyJ8/P/3792fEiBGUKpXRn3ZYtWrV3bU1rly5gsViuftvmQYrhHA1Dz1M0ahRI5KUYiXwsp07Lg0stUM7q+zQhj0tB1KVIjQ01GF9NGwIw4bpOfGXB2Wfs3cJ+GClK1VhSdjVdJ970OPOZLHAga1eDB6gdRJbkREfH8+tW7coXbo0S5fa46cdWrRo4dAl64UQwp4eeiTjiSeeoFmTJkwyyaH5xzXRZKJVy5YULlzYYX088wxUelaxeYksMf1vB7Z6ERmup3t3rZNA8J1BIVm9GqsQQrizR14g7fWhQ/ndbOZPZ6R5hIHYxlmkN/X1cQ3nn/Ea9l7Oaivwp9nM4CFD7Nzy/fr11fHnGm+ibj5yF+YYq2f70qCB9uMxAILuTG+JiorK1PcPHDgQnU6X7tTXxzV8+PC7p1IsFu0XbxNC5Dw69RjHXpu9+CKXtmzhL7P54edXsmA+0Pk/j/Uh80uLO1sqUMVkomDduqzbsMHh/SUlQekyUDYkgQH/u+3w/lzdga2ejO4fxPbt8PzzWqeB+Ph4/Pz8WLVqFc2bN7/nufnz59O5870/7X369JExFUKI7GbBY/0Z/M1333EKGOfAJJ2wXa/k33d3+sj9DNv020k/OGcxcy8vGP0pbFriw+nDOft0VkqSjtnjAmjV2jUKDABfX188PT2JTOciKp06dUIpdc9dCgwhRHb0WEVG6dKlGfvFF3yk0+H4v9Hdz1rgE52OcV99RUknHqvv2hUa1Idv3spNQlzOXZxr1tgAYm4a+eZrrZPcK3fu3OkWGUIIkVM89gn9oUOH0rFjRzobjRxxZCI3cwjoajTSrWtXBg8e7NS+9XqYPRvMCQZ++CgQpf3lTJxu20pv1i/wYdo0KFZM6zT3CgoKyvSYDCGEyA4yNGpw6vTpVKxRg4YmE0cdlciNHAZCjUYq16rFD1OmaJKhYEFYuBD2bvZi5mcBj/6GbOTgH558/34gb78N7dppneZ+QUFBciRDCJGjZajI8PHxYfVvv1G2WjUamEzscFQqN/AH0MBopGLNmqxcuxZvb+2mk9avD3PnwLp5vswZ7+/0Rbq0cGiHJ+PeyE2nzvD551qnSZ+/vz+xsbFaxxBCCM1keP6jr68vq3/7jZDGjWlgMDDDEalc3FSgoV5PnWbNWLVuHT4+PlpHol07mDkTVs7yY9KIQCypWidynG0rvBnTP4i2bXRMnwY6Fx2OYjAYZOqoECJHy9QiC35+fixdvpy3hw+nr05HD72enHDmORLoptfTX6dj+IcfsmTZMpcoMNJ07w6rV8Hejd580iuYW9ey10XULKkw+4sAvn0vkKFDYc7P4MrrxBmNRlJTs3G1J4QQj5DplZz0ej2ffvopK1asYGOePFQ0mVhhz2QuZjlQ0Wjk9zx5WLVqFaNGjULngn9Cv/gi7NwJKt7EO23ysmuDl9aR7OL6RSMfdglmwwJfZs6EL75w3SMYaYxGoxzJEELkaFleLrJFixYcCQujQfv2tAIaGY38bYdgruIA0NBopDUQ2rEjR06coFmzZhqneriKFWHfPujQTse413Pz+aAgwq+451ENc7KOBd/58+ZLwXjpjOzfBz17ap3q8ciRDCFETmeXNamDgoL4ee5ctm3bRszTT/OcTkdXvZ6D9mhcI/uBTno91XQ64itVYvv27cyeM4fcuXNrHe2x+PjA1KmwaRPcvuLJmy3yMvcrf2Kj3WMZcosFtizz5s2X8rJmlh9jRuvYt1dH2bJaJ3t8UmQIIXI6u/7GqVOnDrv272feL79wtFw5KgONTCZWYVt229WZgRVAQ4OBqsCJcuWYv2ABO/fu5XlXWUoygxo0gEN/w6f/p2Pbr368FpqPOV/5E3HdNY9spCTp2LzEh6HN8vH9h4E0DTVw4gS89ZZrj79IjxQZQoiczu5/1up0Ojp27Mhfhw/z22+/Qe3atNTpKGIyMQzb6QdXooB9wFCgiMlEa50OY926rF+/nr+OHKF9+/YuOfYiIzw8bL+kz5+HTz7W8ccyPwY0zMfng4LYv8XTJWaiXDxlZMaYAF6tm58po3LRuIGBkydg+nQoVEjrdJljMBikyBBC5GiPdYG0rDp37hw///wzP0+fzumLFyni4UGzlBSaA3WBXI4O8B/R2K6YuhpYbTJx1WzmqSefpHufPvTo0YMnn3zSyYmcKyUFli6FH35QbN2qwz+Xomr9JGq8mETFGsl4+Th+oQ2rBc4eM7Fnoxd7Nnpz6YyB4iUU/V/V0bs35Mvn8AgO16dPH65evcratWu1jiKEEFpY4JQiI41SigMHDrB69WpWLV3K/r9tQ0TLeXgQkpxMDaDcnXseO/V5Czh+574b2OXpSVhKCgDVKlemRZs2NGvWjOeee85OPbqXc+dgyRJY8ivs2Q06PZSsYKZclRRKPZPCE0+lUrBYKoYsnl2JvGHg0mkjZ46aCNvvQdh+D+LjdDzxpKLdyzratoWaNW1LpWcXXbp0ISkpiV9//VXrKEIIoQXnFhn/dfPmTXbs2MHOnTvZuW0bBw4eJC4xEYBgk4lSOh35UlMpaLVSAPC/cwdIG36Ztj5HDBALXAeu6/XcMBo5oxS3zGYA/Ly9qfLcc9SsU4eaNWtSq1YtgoODnfVW3cKNG7B1K2zbBr9vUZwI02Gx2MZCFC6eSp6CFnIFW8hTwIqvnxUvX4XBAF6+VlJTdCQn6TCn6EiI1RF9S0/kDQPRNw1cPW8k9nYc4E/BQooX6uh44QWoWxcqVND6XTtOmzZt8PHxYe7cuVpHEUIILWhbZKTn4sWLnDhxgrCwMM6dO0d4eDjXLl7kxrVrxMXFERMXhwKi4+IACPTzQwfk8vfH19eXAoUKUfCJJ8ibNy8lSpSgbNmylClThqJFi2r6vtxRUhIcPw5HjsCJE3D5Mly7BpcuK2JjIS5Wh9kM8fFg8gBfH/D0Uvj7Q/78OooWgQIFwGLZwNSpbTh06DxPPZVzCrumTZtSqFAhpk+frnUUIYTQwgKj1gn+64knnuCJJ56gUaNGWkfJ8by8oHJl2/1eDxsIe/9z8fG1+OknAytXzmbYsGH2jOjSEhMTNb2mjRBCaC0bnQEXrsrX15cuXbowZcoUXOzAmUMlJSVJkSGEyNGkyBBOMWDAAE6cOMEff/yhdRSnSUxMxMsreyzrLoQQmSFFhnCKSpUqUbVqVaZOnap1FKeRIxlCiJxOigzhNP369WPRokVERkZqHcUp4uPjXeoqvUII4WxSZAin6dKlCyaTiTlz5mgdxSlu3bol06SFEDmaFBnCafz8/OjUqRM//PCD1lEcLjY2luTkZCkyhBA5mhQZwqn69evH8ePH2bFjh9ZRHCoiIgKAPHnstXatEEK4HykyhFNVq1aNypUrZ/sBoLdu3QKQIxlCiBxNigzhdH379mXBggVERUU9+sVuKq3IkCMZQoicTIoM4XRdu3ZFr9czb948raM4TEREBCaTCX9//0e/WAghsikpMoTT5cqViw4dOjBlyhStozhM2swSne5hS7ALIUT2JkWG0ES/fv04dOgQe/bs0TqKQ0RERMipEiFEjidFhtBEzZo1efbZZ7PtANAbN26QL18+rWMIIYSmpMgQmnnllVf45ZdfiImJ0TqK3V26dImiRYtqHUMIITQlRYbQTPfu3VFK8csvv2gdxe4uX75MkSJFtI4hhBCakiJDaCYwMJB27doxefJkraPYnRQZQgghRYbQWL9+/fj77785cOCA1lHsJj4+nqioKCkyhBA5nhQZQlPPP/88FSpUyFYDQC9fvgwgRYYQIseTIkNork+fPsyZM4fY2Fito9iFFBlCCGEjRYbQXI8ePUhNTWX+/PlaR7GLy5cv4+XlJetkCCFyPCkyhOby5MlDhw4dmDRpktZR7CJt0Kes9imEyOmkyBAuYeDAgfz999/s3LlT6yhZJjNLhBDCRooM4RJCQkKoUqVKtpjOevr0aUqWLKl1DCGE0JwUGcJlDBgwgIULFxIeHm7XdufMmYNOp7t79/Pzu+81Bw8epHnz5gQGBuLv709oaCh//vnnfa8bPnz4PW2FhITc95ozZ85IkSGEEEiRIVxI165d8fX1ZebMmQ5pf/LkySiliIuLu+fx3bt3U6tWLfz9/Tl+/Djnzp2jRIkS1KtXj/Xr19/z2rFjx6KUQimFwWC4rw+z2cylS5ekyBBCCKTIEC7E29ubHj16MHnyZCwWi1P6tFqt9OnTh8DAQGbOnEnBggUJDg5m8uTJlCxZkr59+5KcnPzY7Z0/f57U1FQpMoQQAikyhIsZNGgQly5dYt26dU7pb9u2bRw9epR27drh7e1993GDwUDnzp25dOkSq1ateuz2zpw5A0CJEiXsnlUIIdyNFBnCpZQqVYqGDRs6bQDo5s2bAahatep9z6U9tmnTpsdu78yZM+TJk4fcuXPbJ6AQQrgxKTKEyxk4cCBr1qzh9OnTD3xNWFgYrVu3JleuXPj4+FC9enVWrVpFaGjo3UGZffv2fWRfYWFhQPqrcxYuXBiAkydPPnZ2GfQphBD/MGodQIj/atmyJU888QRTp07l888/v+/506dPU7NmTXx9fVm8eDE1a9bkwoULDB06lEOHDuHp6UlSUtJj9RUdHQ2Ar6/vfc+lzUKJiop67OxSZAghxD/kSIZwOQaDgT59+jBjxox0i4X333+f6OhoJkyYQKNGjfDz86NChQrMmzeP+Ph4u+VQSgFkaOVOWSNDCCH+IUWGcEn9+vUjJiYm3euZpA0Kbdy48T2P582bl7Jly2aon8DAQIB0i5O0x9Je8yhWq5Vz585JkSGEEHdIkSFcUoECBejQoQMTJky45/Hk5GRiY2Px8vJKd1GtjA64TCtK0q6c+m9XrlwBoHTp0o/V1oULF0hMTMxwoSOEENmVFBnCZQ0dOpSDBw+yffv2u495enri7+9PUlLSfYtqARleLbR+/foA7N+//77n0h5r2LDhY7V1/PhxAMqUKZOhDEIIkV1JkSFcVpUqVahZsybffvvtPY83bdoU4L61NK5fv56hmSAAdevWpXz58ixevPie8R8Wi4X58+dTtGhRmjdv/lhthYWFUaBAAZm+KoQQd0iRIVzaG2+8wdKlSzl37tzdx8aMGUNQUBBDhw5lw4YNxMXFceTIEXr37k2BAgUy1L5er2f69OlERkbSu3dvrl+/TkREBIMGDeLUqVNMnToVLy+vx2orLCyMcuXKZah/IYTIzqTIEC6tXbt2FCxYkB9++OHuYyVLlmTnzp1Uq1aNdu3akT9/fvr378+IESMoVapUhvsICQlhx44d3L59mzJlylCsWDFOnTrFli1b7htc+jBhYWEyHkMIIf5F1skQLs1oNDJgwADGjx/Pxx9/fHc9i9KlS7N06VK79VO5cmXWrFmTpTbCwsJo166dnRIJIYT7kyMZwuX179+fxMRE5s6dq3WUB4qIiODmzZtyJEMIIf5Figzh8oKDg+ncuTMTJky4u0BWZgwcOBCdTpfu1NfHNXz48LvLlv/7SrFpM0tkTIYQQvxDigzhFoYNG8axY8fuXtDsv+bPn49Op2PTpk0kJyffc+2Sbt26oZS6e09v6uvjGjt27D1t7dq1C7CdKvH19U33GihCCJFT6VRW/jQUwonq1q1LYGAgy5cv1zrKfd5++222bNnCvn37tI4ihBCuYoEcyRBuY8iQIaxatSrDa2E4w9GjRylfvrzWMYQQwqVIkSHcRuvWrSlZsiRfffWV1lHus3//fqpUqaJ1DCGEcClSZAi3odfrefPNN5k1axbXr1/XOs5dFy9e5ObNm1JkCCHEf0iRIdxK7969yZ07N5MmTdI6yl379+9Hr9dTqVIlraMIIYRLkSJDuBUvLy8GDhzI999/n6VZIva0f/9+ypQpg7+/v9ZRhBDCpUiRIdzO4MGDSU5OZsaMGVpHAWQ8hhBCPIgUGcLtBAUF0bt3b77++mtSU1M1zWK1WtmzZw9Vq1bVNIcQQrgiKTKEWxo2bBiXL19m8eLFmuY4dOgQkZGR1KtXT9McQgjhiqTIEG6pePHitG3b9u4KnFr5/fffCQoK4umnn9YsgxBCuCopMoTbeuedd/j7778fuNS4M2zZsoV69eqh18v/SkII8V/yySjcVtWqValfvz7jxo275/GYmBg+++wzbt++bdf+mjRpwogRI9i7dy9KKaxWK9u3b5dTJUII8QBy7RLh1tauXUuzZs3466+/KFKkCBMmTOCbb74hLi6OQ4cO2fU0RokSJTh//jxKKfLnz0+DBg345ZdfOHjwoKyRIYQQ91sgRYZwa0opKlSoQEBAAIcOHcJsNt+dcbJ69WqaNWtmt77Kli3LiRMn7v7bw8ODlJQUAgICaNmyJR06dKBx48Z4eHjYrU8hhHBjcoE04b4uXLjAkCFDOHPmDAcOHCAxMfFugWE0Grl06ZJd+/P09Lzn3ykpKYDt9MyCBQto2bIlwcHBLrUaqRBCaMmodQAhMurEiRN89tlnzJkzB71ej9lsvu81BoPB7kWGl5fXA58zm83o9XpMJhNt27a1a79CCOGupMgQbkUpxUcffcSiRYsAsFgs6b4uNTXV4Ucy0ss2f/58ChYsaNd+hRDCXUmRIdyKTqfjl19+Qa/Xs3jx4gcWGRaLhfPnz9u1b29v7wc+p9frGTlyJI0aNbJrn0II4c6kyBBux2AwMHfuXICHFhr2LjIedCTDZDIREhLCBx98YNf+hBDC3cnAT+GW0gqNdu3aYTAY0n3N9evX7boaqLe3Nzqd7r4cAQEBLFy48IE5hBAip5IiQ7itRxUaKSkpRERE2K0/T0/P+4oMpRQLFy6kQIECdutHCCGyCykyhFt7VKFhz8GfHh4e9ywfrtfrGT16NA0aNLBbH0IIkZ1IkSHc3sMKDXsWGf8+kmEymWjYsCHvvvuu3doXQojsRooMkS2kV2iYTCaHFBkGg4GgoCDmzZsnF0YTQoiHkNklD2GxWLhx4wbh4eHcvn0bs9lMYmIiSUlJdwf8AeTOnZugoCAKFCjw0GmOwrEMBgM///wzKSkprFixAovFkm6RkZgIV69CRATExIDVCmnXUsuVC/R6CAiAPHmgUCFI26Wenp6kpKRgMBhYunQpwcHBTnx3QgjhfqTIAK5evcq+ffs4fvw4x48f5+Cxg1y6dImo8CiUNWOzE3xz+VKgcAGeLvs05cuWp0KFCjzzzDOUL19e/up1ApPJxIIFC+jYsSNLly5l587LjBkDhw/D0WOKSxd1REdnrM3AQCj6hEJZbVNYe/b8gpIla9o9uxBCZDc58gJpV65cYc2aNWzdtpUtf27hyrkroAOPJzywlLVgKW+BYkAhoACQD8iNrSTzArwBCxBzp8Fo4BZw/c79EujCdHgc98B80ow1xYpfoB+1a9Xmhdov0KRJEypXrnzfTAWRNXFxsG4dbNgAv29J4dTJjkAEBYr+TtGnUilSyky+whaC8lnJnc9CriArXr4KHeAbYAUgPkaPAhLjdMRE6om6aSDihp6bVwzs2fQVt67tJyVpGUrpKFNWUb+ejtBQaNoUfHy0fPdCCOFycs5VWE+ePMm8efP4deWvHPnrCHpvPbraOlJrp8LzQHXA3wEdpwKHgT9At0OHaZuJlKsp5CuSj7Yt2tK+fXvq1asnRzkyKSkJFi+GhYtg/Xowm6FMJTPlqiZTrkoiR/aMpsc779mlr10b1lCxRm30+kDCDnhwbJ8Hx/d6cvKQCQ8PaNwYOnaAtm3hESuQCyFETpC9i4zExEQWLFjAjzN+ZPcfuzEWNGJuaYaXgAbYjko4mwL+AlaBaYUJ834zRYoXoV+vfrzyyisUKVJEg1Du5/Rp+OEHmDkTYmLh2drJVA9NolrDJAJyW+++Tlmt6BxcwMVE6tmzyYs9G734e4cnuQKgd28YMABKlnRo10II4cqyZ5ERExPDzJkz+fSLT4m8FQkvgrWHFdrgeqNQwoBZYJplwhpppXOnzgx/bzgVKlTQOplLOnsWxn4OM2ZA7mArdV5KoEmXBIILpr+0uLNF39Lz+1IfNizw4eZVA21fhk//D8qU0TqZEEI4XfYqMhISEvjyyy8Z++VYzAYzqYNS4Q1sYypcnRmYB6YvTKQeT6VNuzZ8+fmXFC9eXOtkLuHWLRg+HGbNgkLFU2k3MI7aTRPRuehZJqsF/ljtzZIf/Ll2wUCfPjBmjG3GihBC5BDZp8hYsGABb777JjejbpI6IhUG45gxFo6mgOVgHGFEd17HW0Pf4sMPP8TX11frZJpQCqZNg/feA4Onla5vxVCnuesWF/9ltcD2Vd7MGR+AzqLn88/hlVdAxvwKIXIA9y8ywsPD6du/L6tWrELXW4f1U6ttRoi7MwOTwfiJkcJ5CjPvp3nUqlVL61ROdesW9OoF636DZt3i6fh6LN6+7vnjmhCnY8G3/qyZ60vzZraxJHJUQwiRzbl3kbF27Vq69upKnE8c5llmqKt1Ige4Boa+BtRvivdHvM+oUaNyxEyUP/+E9u3Bqrcw5MtoylRO0TqSXRzf78GEt3Nj0utYslhHSIjWiYQQwmEWuO1vqwkTJtDipRZEN47G/Hc2LTAACoJllQXrJCtjxo2hVdtWxMXFaZ3KoZYuhdBGULRcMuOW3so2BQZAuSopfLnsJoWeSqFBQ1i5UutEQgjhOG5XZCileP2N13lz2JtYx1hRsxUEaJ3KwXRAf7ButvLbzt+o+UJNbt26pXUqh5g503YEo16bBN6ZGHl3kazsxC+XlfcmRVKnRQJt2sLPP2udSAghHMPtTpcMHTqUid9PxPqLFV7WOo0GzoOpoYnSAaXZvnk7uXPn1jqR3SxbBu3aQZtX4+g8JFbrOE4xZ7w/K2b6sWwptGihdRohhLAr9zpdMnLkSCZOmoh1bg4tMACKgXmTmZORJwltEkpSUpLWiexi1y7o1BkadUzIMQUGQNdhsTRom0CHjrBvn9ZphBDCvtzmSMaqVato2bIl6kcF/bRO4wJOgbG6kR7tejB96nSt02TJ7dtQ6VlF8JMpjJgc6TbTU+3FaoHRr+Yh5rqJg3/p8HfHqddCCHE/9ziScenSJbr06IKul04KjDRPQepPqcyYPoM5c+ZonSZLBr8OsfGKQWOic1yBAaA3wOufRxEZrXhjiNZphBDCftziI33Y28NICk7COsnJgwDnYBt0mXb3S+c1B4HmQCC2xb9CgT/Ted3w/7Rlj6mLLYHB8Pqw14nO6PXLXcTOnTB3Drw66ja58mg3yHPPxnW8XLbQ3bs5Odmp/QcGW+n3yW1+mgV79zq1ayGEcBiXLzK2bNnC4oWLMX9ttl1iXQuTsa3E+d+Zo7uBWtiKi+PAOaAEUA9Y/5/Xjr3ThgIMdsz2P4jTxfHJqE/s2KjzvPUWPF0jhWoNtB1bUj20CUvCrlK9YWPNMoQ0SqJcFTNvv61ZBCGEsCuXLzL+N+Z/GBoZbEcLXIkV6IPtCMZMoCAQjK0gKQn0BZzxx3AgpI5M5fsfviciIsIJHdrPjh22Ixld3ozROorL6DI0hm3bZBCoECJ7cOki48yZM2zZtAXLG65xhc17bAOOAu249wiLAegMXAJWOSlLL1uGWbNmOalD+5gxA4qVSeWpSmato7iMclVTeKJUKjNmaJ1ECCGyzqWLjJ9//hlTYRM01TpJOjbf+Vo1nefSHtvkpCw+YO5iZtrsaU7qMOssFli0COq3TdA6isup1zaB+fPBmv3WIRNC5DAuXWRs+H0D5hfN9h3DABABDMN2WsMTKIJtwOYsIPEx2wi787VIOs8VvvP1ZOYjZlhjOHH4hNucMjl0CGJioFJtx59Tio2OYtbYT3itUU06Pv0k/eo+xye9O/D70gWkPGCdkahb4Yx/cwDdq5WlZ43yjBnQg+sXzzs8K8AzISlERcGxY07pTgghHMZli4yUlBT27dmHqmPnZTyuA9WAX4AJwC1gP7bBmr2BHx+zneg7X9O7AnvaLJSozIbMhDqADv78M72pLa5n507wC1AUKZHq0H6ib4XzbrumbF+1jFc++B+zdh1l3K+/UbF6Lb4b8SbrF6S/pvfMMR/Tomc/pm07wNsTpnBs326+fus1h2ZN82QZMz5+ih07nNKdEEI4jFHrAA9y9epVUpJSoJydGx6BbRbIAiBtGWd/4EPSn3qaGWl1kc5O7T2OQDAVMHHu3Dkndpp5Fy5AoSctDl8XY874zwi/fJG3vv6BqvUaAeDt60e7gUMJO/DguaIN23ehzLNVAHg65Hmq1gtl+6qlxERFEpA7yKGZ9QYoUNTChQsu+7+nEEI8Fpf9FLt7AbBgOze89M7X9MZ5rM1AO4F3vsan81z8f17jJLq8Om7evOncTjMpIgJ8Ax0/oHf3RttOrfxCg/ue+3Dq3Ad+X6mnn73n30H5CwIQFX7D4UUGgH9uK25y5ksIIR7IZU+XJCTcGRDoY8dGk4HbgBe2oxdZUfbO18vpPHflztfSWewjg5SfIjbWPa77kZAAHp6OXdHenJJCQmwMJk9PvH3TW0ntwXz97r20r15vOyxlddJoTE8vRdx/12URQgg347JFRlDQnb8WI+3YqCeQC0gCsvq7uP6dr/vTeS7tsYZZ7CODdLd0BAfb+9CPY+TODfExjv3xM3l44OMfgDk5mcR49/qNHR+jJ08erVMIIUTWuGyRcfeXZbidG25z5+uadJ6rDLz5mO3UBcoDi7EVLWkswHygKE5fQMwSbnGbIiM4GGKj7D1t6H41Qm3nxQ5s3Xzfc2+3acTMz0Y6PENmxERKkSGEcH8uW2Tkz5+foPxBYO+VDz8DimMrJlZjO6JxGXgNuMbjFxl6YDq2Iy29sc1aiQAGAaeAqdhOyzjLaUiNSqVSpUpO7DTzKlSAy+cMJCU4dnRst7dGkK/IE8z8bCT7t2wkMT6OiOvXmDJqBFHh4bzU61WH9p8ZCXE6rlwwULGi1kmEECJrXLbI0Ol01KtTD8M2O/+1WwDYC3QEXgfyANWxTTfdDjyRgbZCgB3YxnmUAYphKzC2AM6+BMZ28PTxpGrV9FYHcz21aoElFc4cMTm0n8DgfHyxeC21m7Vk2qcf0rNGBd5r34z4mGg+nbeM4IK2RU1O/r2fl8sWYs+m3wDoVKk48775HICXyxZi6dRJgO3ox5gBPRya+dQhD6wWqFnTod0IIYTD6ZRSjh19lwXTpk1jwBsDsFyxQG4NAswBumO7HskAO7ZrxLYq6C47NtnMSANdA35b/Zv9GnWwp0orytRM4JX35dol/zb1f7k4/5cPx2UxLiGEe1vgskcyADp16oSnydO2Eqd4sLNg+c3CoP6DtE6SIb176di+0gdzijMXFHFtKUk6/lztTZ9XtE4ihBBZ59JFhp+fH72698I0wfT4y307wkBsC2tlbBbkvYbfaUOHbXCoHem+0FGwaEGaN3e1S9U+XM+etvEH21Z4P/rFOcTvS71JStTRvbvWSYQQIutcusgA+PijjzFFmWCcBp13w7Z6Z9o9K7Mgx/6nLXudKjkITIMvRn+BweD42Rr2VLgwvNoPFkz0JzlRjmYkJehY/L0/gwdB/vxapxFCiKxz+SIjf/78fPz+xxg+N8BxrdO4GDMYBxipWr0qXbp00TpNpowcCcnxehZPzsphouxh4SR/UpP1vP++1kmEEMI+XL7IABg2bBjPVXoOUztT1o4mZDO6t3WYjpr4afpP6HTueSQgXz748ktYNs2Pw7s8tI6jmaN7PFg5y5evv7atISKEENmBWxQZJpOJXxf+it9NPww9DODYC3e6hxmgJipmTJ1BuXL2voqcc/XvD61aw3fDcxNx3b1O+djDzasGJrydm3bt4BUZ8CmEyEbcosgAKFKkCMuXLMf4mxF9Lz045xISrmke6F7V8dGHH9GpUyet09jF9GmQN0jH6H5BxN12mx/LLIuJ0jO6Xx4KFdAxdYrWaYQQwr7c6tO8Tp06rF6xGsMSA/pu+nuX884ppoO+p5633nyL//3vf1qnsZvcuWHDeh3WZCOj+wURE+VWP5qZcjtCz6d9g9Bb9Py2TkdAwKO/Rwgh3InbfZI3bNiQtavW4rPWB1MDE9zQOpGTWIC3gX7wwYgPGDdOi+k2jlW4MGzaCOZYEx91CebGpex76uT6RSMfdA5GJRnZvElHwYJaJxJCCPtzuyIDbIXG3p17KXSzEKYqJnCfRS4z5yIYGxkxTTIx5+c52eoIxn899RTs2AHBgQbe7xTMga2eWkeyu32/e/Fe+9sUzmdg5w4dJUponUgIIRzDLYsMgLJly3Jg9wFaP98amoJukC7rl293NQr4CQzPGHjy+pPs/GMnXbt21TqVwxUoANu3QcvmesYMCGLW2ADMye45e+bfUpJ0zBgTwNjXDCQnVMHLqy5nz9pxbXkhhHAxbltkAAQFBbFw/kLmzZ2H/3x/TKVNtiujZodBoXvAWNuI7hUdg3oN4vD+w1SpUkXrVE7j5wc//WS7/77YlzdfysuBbe57VGPfFk/efCkv25b68vPPAfzxxwZMJgM1a9akUaNGHDp0SOuIQghhd25dZKTp3LkzZ0+epd/L/dAP0GN6zgSLcM9i4yDoO+ohBKqbqrNv7z4mfDMBb++cufR29+4QFgbPhxgY/WoQY18L4twxx1651Z7OHDExpn8Qnw0Iot7zBsLCoGtXqF69Ops3b2bDhg3cunWLypUr06FDB86fP691ZCGEsJtsUWQA5MmTh0nfTeLw34d56amX0HXS4VHWA34EXP0in1ZgPRiaGuA5KHeyHEsWL+HPrX/y3HPPaZ1Oc4ULw8IFsGEDqBhP3nk5mM8HBRF2wHUX7zq214PPBgTxbrtg9Ime/P47/DIPChW693WhoaHs37+f+fPns3//fipUqMDw4cOJjo7WJLcQQtiTS1/qPStOnjzJF+O+YPac2Vj1VqwvW1G9FNQFXGXSwklgDphmmTBfMvN8vef54L0PaNy4sduu4OkMq1fD0KFLOH26JcVK6wjtmMDzzRPxD9T20FVstJ7tq7zZMN+Hi6eN1AiBkR9D06aP9/0pKSnMmjWLjz/+GLPZzLvvvssbb7yRY49iCSHc3oJsW2SkiYqK4pdffmHKzCn8ve9vTHlMpDZLRb2koAGQx4lhkoE9wCrwWOFBSlgKeQvlpU+PPvTq1YsyZco4MYz7+uSTT/jf//7H11+v4vDhZvwyH5KToWL1FGq8mEiVuskEF7TzpW4f4NY1A/u3eLJ7vTdH9nrg5Wk7HfLqq5DZITRxcXFMmjSJ0aNHkytXLj766CP69OnjdhfAE0LkeNm/yPi3kydPsmLFCn5d+Su7/9yN1WrFo6wHKbVSIASoAJQDAu3QWQpwAttF3faBaYcJ634rliQLTz71JO1bteell16idu3a8ssjAz7++GM+/fRTJk6cyKBBgwCIjbUd3VjyK6xZAwnxUKCohXJVUijzXArFypgpUioVb9+s/agnxuu4dNrIhRMmwg54ELbPg+uXDfj6QrPm0O5laNbMNmjVHm7evMn48eP5+uuvKVmyJKNGjaJ9+/b2aVwIIRwvZxUZ/xYVFcWff/7Jjh072PLnFg4eOEhiXCIAHgU9oASY85tRhRTkAwIAb8AE+GErIuKxXUclFtuiYOFgumpCf1lPyrkUVKrCYDRQsmxJ6teuT61atahduzYlS5bU5D27M6UUw4YNY+LEiUybNo1evXql+7rERNi1C7Zuha1bFXv26kiIB50O8hexEFzQQlB+C4HBVgKCrPgGWNHpwNffdqolPlaPUhAfoycmUk/0LT2RNwzcumrg+mVbMejrC9WrK+rW1VG3LoSEgJeX4977yZMn+fDDD1m8eDE1atTg888/54UXXnBch0IIYR85t8j4L6UUFy5cICwsjGPHjnHx4kWuX7/O+avnuRF+g/i4eBITEklNSSUpPgmD0YC3vzdGkxFfP1/y58tPoXyFKFKoCIUKFaJs2bKUK1eO0qVL4+HhugMU3YFSijfeeIPJkycza9YsunXr9tjfa7XC+fNw5AgcPQqXLsHlK3D1qiIiAm7fBmXVcfu27fW5coFOD7lyKYKDoWBBHUWLQJEiULEiVKgAxYvbihZn27t3L8OHD2fz5s2EhoYyfvx4nnnmGecHEUKIxyNFRkYppdDr9SxcuFAOXTuBxWKhX79+zJ07l/nz59OmTRutI2lu48aNvPvuu/z999+8/PLLfPHFFxQrVkzrWEII8V8Lss0UVpH9WCwWevXqxbx581i0aJEUGHf8e9rrgQMHKFOmDEOGDCEqKkrraEIIcQ8pMoRLSklJoUOHDvz666+sWrWKli1bah3Jpeh0Otq3b8/x48eZOHEiCxYsoGTJknz++eckJiZqHU8IIQApMoQLSk5Opn379qxfv55Vq1YRGhqqdSSXZTKZePXVVzl9+jTvvfceY8aMoXTp0kyZMoXU1FSt4wkhcjgpMoRLSUhIoEWLFmzbto0NGzZQv359rSO5BT8/P9577z3OnDlD165deeONN3j66adZtGgRMuxKCKEVKTKEy4iLi6NFixYcOHCA9evXExISonUktxMcHMzYsWMJCwujWrVqdOzYkZo1a7J161atowkhciApMoRLiI6OplGjRhw7dowtW7ZQrVo1rSO5tWLFijF79mx2796Nr68v9erVo1GjRvz9999aRxNC5CBSZAjNRUVF8eKLL3LhwgU2b97M008/rXWkbKNatWps2rSJDRs2EBkZyXPPPUeHDh04d+6c1tGEEDmAFBlCUzdu3KBu3bqEh4ezfft2ypcvr3WkbCk0NJR9+/bdnfZatmxZ+vfvz82bN7WOJoTIxqTIEJq5du0aDRo0IDk5me3bt8ty6w7232mvK1asoGTJknzy/+zddXhT1xvA8W+sXlqK62BFirs7tDjDKcNt6BiyDRkwOkPGGOvYcHeKDWc4FPehhSJjWJG6N03u74+s/a1QIIUkN2nP53ny8JDc3vPenJubN+ce8fMTw14FQTALkWQIsrh//z716tVDr9dz6NAhChUqJHdIWcZ/h71OmDCBWbNmUbx4cTHsVRAEkxNJhmBx9+7do1GjRri6unL06FHy588vd0hZkrOzc+qw1x49eohhr4IgmJxIMgSLCgoKol69enh4eLB//35y5cold0hZXsqw15s3b1K/fn26du1KrVq1OHz4sNyhCYJg40SSIVjM9evXady4MUWKFOHgwYPkyJFD7pCE//jggw+YP38+p0+fxsXFhUaNGuHj48OlS5fkDk0QBBslkgzBIi5cuED9+vUpXrw4u3fvJlu2bHKHJLxG1apV2b9/f+qw1ypVqtClSxfu3r0rd2iCINgYkWQIZnf27Fl8fHyoVq0ae/bswdXVVe6QBCP8d9jrxYsXKVWqFIMGDeLZs2dyhyYIgo0QSYZgVkePHqVJkybUqlWLLVu24OjoKHdIQgakDHu9fv16mmGv48aNIzo6Wu7wBEGwciLJEMzmzz//pHnz5jRv3pwtW7bg4OAgd0jCO/rvsNeJEycyd+5cSpUqJYa9CoLwRiLJEMxi586dtGvXjnbt2rFmzRo0Go3cIQkmkN6w17Jly4phr4IgpEskGYLJbdiwgfbt29OzZ09WrVqFWq2WOyTBxP477LVBgwZ07dqVmjVrcujQIblDEwTBiogkQzCpdevW0a1bN/r378+8efNQKsUplpmlDHv966+/+OCDD2jcuLEY9ioIQirxDSCYzKJFi+jevTujRo1i7ty5IsHIQsqWLUtAQAD79u0jPDxcDHsVBAEQSYZgIvPmzWPQoEF8+eWX/Pjjj3KHI8jE29ubs2fPsm7dOi5duiSGvQpCFieSDOG9/fTTTwwdOpTJkyczbdo0ucMRZJYy7PXatWvMnj2b7du3i2GvgpBFiSTDQlatWoVCoUh9uLi4vLLNpUuXaNWqFe7u7ri6uuLt7c3x48df2W7cuHFp9lWzZk1LHEK6pk+fzpgxY5g5cyZff/31W7fPrO+D8KqUYa/BwcFMnDiRefPm4enpyfTp00lKSpI7vNcy5hwF2LVrFyVKlHhjx2ZxjgpZnUgyLGzu3LlIkkRMTEya50+fPk3t2rVxdXXlxo0b3Lt3jw8//JCGDRuyd+/eNNtOmzYNSZKQJAmVSmXJ8NPw8/Nj/Pjx+Pv7M2rUqAz9bWZ6H4Q3+++w1379+jF58mSbWO31defonTt3+Oijjxg/fjxPnz594z7EOSpkdSLJsAJ6vZ7+/fvj7u7O0qVLyZcvHzlz5mTu3Ll4enoyYMAAEhMT5Q4zlSRJfP7553z//fcsXryY4cOHm2S/tvY+CBmTI0cOpk2bxq1bt2jYsCEff/wxNWrU4ODBg3KHliGTJk2idu3anD9/XkyRLwhvIZIMK3D06FGuXbtGp06d0ky7rVKp+Pjjj3nw4AE7duyQMcL/kySJESNG4O/vz9KlS+nbt6/J9m1L74Pw7goXLpw67LVIkSI0adIEHx8fLl68KHdoRlm8eDHjxo0T878IghFEkmEFUn7JVa1a9ZXXUp47cOCARWNKj06nY8CAAcyfP5+AgAB69uxp0v3byvsgmEaZMmUICAjg2LFjxMfHpw57vXPnjtyhvZFYf0cQjCeSjPcUGhrK6NGj8fT0xN7enoIFC+Lt7c2yZcuIj483ah9BQUEAFCxY8JXXChQoAMCtW7dMF/Q70Ol09OvXj9WrVxMQEECHDh1SXwsKCqJdu3a4ubnh5ORE9erV2bFjB97e3qkd3gYMGPDWMmzhfRBMr06dOgQGBrJ+/fo0w17f1t8hI0x1jgqCkDGive89hISEULt2beLj41m4cCENGjQgLi6OhQsX0rdvXyIiIhg5cuRb9xMREQEYOsi9LKVne3h4uClDz5CkpCS6devG7t272b59Oz4+Pqmv3b59m1q1auHs7MzGjRupVasW9+/fZ+TIkVy+fBl7e3sSEhKMKsfa3wfBfFKGvbZr146lS5fi5+fHmjVrGDZsGF999RXZsmV7532b8hwVBCFjREvGexg/fjz37t3D39+f1q1b4+rqSp48eZg4cSLNmzc3SRkpve8VCoVJ9pdRiYmJdOnShT179rySYAB89dVXRERE4O/vj4+PDy4uLpQpU4Y1a9YQGxtrsjjkfh8Ey3h5tdf/Dnt9106/ljpHBUF4lUgy3sOWLVsAaNGixSuv7d6926hWDAB3d3eAdC94Kc+lbGNJcXFxtGnThiNHjrBv3z4aN278yjZ79uwBoFmzZmmez5UrF15eXhkqz1rfB8HynJycUoe99u/fHz8/P7y8vFixYkWGh72a8hwVBCFjRJLxjrRaLZGRkTg4OLz3MLaUC93Dhw9fee3Ro0cAlChR4r3KyKjY2FjatGnDuXPn+PPPP6lVq9Yr2yQmJhIdHY2Dg0O6ExZlz549Q2Va4/sgyCtl2OvNmzdp2rQp/fr1y9CwV1Ofo4IgZIxIMt6RRqPBzc2NhISE954quVGjRgCcP3/+lddSnmvSpMl7lZERkZGR+Pj4cPXqVQ4fPkz16tXT3c7e3h5XV1cSEhJembAIyPB6Fdb2PgjW43XDXi9cuPDGvzP1OSoIQsaIJOM9tG/fHjBML/yySpUqGT0LZoMGDShdujQbN25M0wFNp9Oxbt06ChUqRKtWrUwT9FuEh4fTtGlT7t27x8GDBylfvvwbt0+5VZTSJJ0iJCQkwyNBrOl9EKxTyrDX48ePk5CQQNWqVd867NWU56ggCBkjkoz3MHXqVIoWLcqoUaPYuXMn0dHRPHz4kKFDh/LkyROjkwylUsnixYsJCwujb9++hISEEBoayrBhwwgODmbhwoU4ODiY+WgMv+oaNmxISEgIgYGBlClT5q1/M2XKFDw8PBg5ciT79u0jJiaGq1ev0rdvX/LmzZuh8q3lfRCsX+3atQkMDGTv3r3cuHEjddhrSEjIK9ua8hwVBCFjRJLxHvLmzcvZs2fx9fVl+PDh5MiRg+rVqxMeHk5gYCCFCxc2el81a9bkxIkTREZGUrJkSYoUKUJwcDCHDx9+pcOaOYSEhNC4cWOioqI4fPgwxYoVM+rvPD09OXnyJNWqVaNTp07kyZOHQYMGMX78eKP38V9yvw+CbfH29ubixYv89ttv7Nixg2LFijFu3DiioqJStzH1Obpjx47UuTUePXqETqdL/f+iRYtMeXiCYPPEPBnvKUeOHMyaNYtZs2a9974qVaqU7q0Xc/vnn39o0qQJKpWKY8eOpU58ZawSJUqkjrQxBbneB8E2qdVqBg4cSI8ePZg9ezbTpk1j8eLFfPHFF4wcORJ7e3uTnqOtW7e26oXdBMGaiJaMLO7vv/+mUaNG2NnZcejQoQwnGIJgLdIb9lqyZEkWLFiAXq+XOzxByJJEkmFhQ4YMQaFQpDuczljjxo1LbZ7V6XTvvJ+bN29St25d3N3dOXLkCPny5XvnfWWUNb0PQubi4eGRutprs2bNGDp0KDVq1MjwujfiHBUEE5CEDNHr9RIgBQQEyB3Ke7l+/bqUL18+qWrVqlJoaKhJ97127VoJSPPo37+/ScsQBGNdu3ZN6ty5swRI3t7e0vnz58U5KgiWsU60ZGRBFy9epH79+hQrVoyDBw/i4eFh0v137doVSZLSPESHOEEupUuXJiAggBMnTpCYmEjVqlXZvHkzwcHB4hwVBDMTSUYWc+7cOby9vSlbtiy7du1679lKBcFW1KpVi6NHj7J3716CgoIoXbr0a4e9CoJgGiLJyEICAwNp0qQJNWvWZPfu3e91r1kQbJW3tzeXLl1i0aJFrx32KgiCaYgkI4s4fPgwLVu2pEGDBmzevFlMaiVkaUqlkl69ehEcHMykSZOYP3/+e6/2KgjCq0SSkQXs3r2bFi1a0Lp1azZv3oy9vb3cIQmCVRDDXgXBvESSkclt376d9u3b06lTJ1atWoVaLeZfE4SXpTfstUKFCmzYsEHu0ATBpokkIxNbv349HTt2pE+fPixfvhyVSiV3SIJg1QoVKsT8+fO5cuUKpUqVokuXLvj4+KS7MrAgCG8nkoxMavXq1fTo0YOBAwcyd+5clEpR1YJgrFKlSqUOe01KSqJatWp06dKF4OBguUMTBJsivnkyoQULFtCrVy8+//xzfvvtNxQKhdwhCYJNqlWrFkeOHGHv3r3cvHmTMmXKMGjQIJ48eSJ3aIJgE0SSkcnMmTOHwYMH8/XXXzNt2jS5wxGETCFltdfVq1ezd+9eihcvzrhx44iMjJQ7NEGwaiLJyESmT5/Op59+yk8//cTkyZPlDkcQMhWlUknnzp25du2aGPYqCEZSSJJYs/hNvL29OXPmTJqlnRMSErCzs0vTz0Gj0XD58mUKFiwoR5hMnz6d8ePH88svv/DZZ5/JEoMgZCVhYWH8+OOP/Prrr+TKlYsJEybQv39/0cFaEP5vvWjJeIsWLVoQHR1NTExM6iM5OZm4uLjU/8fGxuLp6WnWBOP+/fuvfW3ixIl89dVXLFq0SCQYgmAh/x322rx5czHsVRDSIZKMt/j444/fOjJDpVLRu3dvs8Vw5swZKlWqxKVLl9I8L0kSI0eOZNq0aSxZsoR+/fqZLQZBENJXsGDB1GGvpUuXpkuXLtStW5fjx4/LHZogyE4kGW+RP39+ateu/cZEQ6/X07lzZ7PFMGnSJMLDw2ncuDHXrl0DDAnGp59+ypw5c1i3bp1ZkxxBEN4uZdjryZMnUalU1KtXTwx7FbI8kWQYoWfPnq8dBqpSqWjUqBF58uQxS9nnzp1j3759AERHR1O/fn2uXr1K3759WbRoEQEBAXTq1MksZQuCkHE1a9YUw14F4V+i46cRwsPDyZ07N8nJya+8plQqWbJkidlaEnx8fDhy5AharRYAtVqNk5MTWq2WP/74g6ZNm5qlXEEQ3p9er2fTpk2MGTOGZ8+eMXz4cMaPH4+bm5vcoQmCJYiOn8bInj07Pj4+6fYaV6lUtGvXzizlnjx5kv3796cmGEBqp1MXFxdKlChhlnIFQTCNlGGvN2/eZNasWSxZsiR12GtCQoLc4QmC2Ykkw0g9evR4ZVVGtVpN69atzfarZNKkSWg0mleeT05OJiIigoYNG/L48WOzlC0IgunY2dkxcOBAbt68yYABA/jmm29SV3vV6XRyhycIZiOSDCO1a9fulSXSdTodPXr0MEt5J06c4MCBA2laMf5Lq9Xy6NEjGjVqxPPnz80SgyAIppU9e3amTZtGcHAwzZs3Z9iwYWLYq5CpiSTDSE5OTrRt2zZNy4KjoyMtWrQwS3kTJ05867LsOp2OW7du0b9/f7PEIAiCeRQoUCDNsFdfX1/q1KnDsWPH5A5NEExKJBkZ0L1799SWBY1GQ6dOnXB0dDR5OSdOnODQoUPpdjQFUpOPkiVLMn/+fPErSBBslJeXV+qwV41GQ7169WjTpg23bt0y6u+joqLMHKEgvB+RZGRA8+bNyZYtG2C4XdG9e3ezlPPVV1+l24qR0opSpUoVtm3bxvXr1xk4cOArt3EEQbAtNWrU4PDhw+zbt48HDx6kDnt9W5+r1q1bs3LlSgtFKQgZJ5KMDNBoNHTt2hUw3Ftt3Lixycs4fvw4R44cSdOKodFoUCgUeHt7c/LkSU6dOkWbNm3EEu6CkMl4e3tz4cIF1qxZw759+1JXe42IiHhl2+3btxMYGEjfvn3ZtWuX5YMVBCOIeTL+IyYGoqP//0hMhLi4/7+ekADnzh3mm28a0bz5p3zyyWz+bdgAQK0GV1ewtzf8my0buLtDRnKBBg0acPz4cXQ6HWq1GqVSSb9+/fjiiy/w9PQ02bEKxgsLCyMkJITIyEhiY2PR6XSpzdTZsmVDpVLh7OyMm5sb+fLlI3v27DJHnLlllfpISkpi2bJlTJo0ieTkZMaMGcOIESNwcHBAp9NRunRp7ty5g16vR6PRcODAAerWrWvRGMPC4MkTiIiA+HjDNTI+/v/XQpXKcA3Mk8fwEGvHZTnrM3WSkZQE9+8bHiEh8OyZ4QPx7Bk8fQqPHktERkJ0lILISDDundADhYEAoLZRcbi4gIurhJsb5MqloEB+wwcuVy7Inx9y54ZCheDRoyO0atUQMFwsR4wYwfDhw8mVK9e7vQGC0WJjYzl37hzXr1/n+vXrXLlxhVu3b/Ei5AXaxPRH+LyOnYMdOfLmoGSxkpQrVY7SpUtTpkwZqlSpgpOTk5mOIHMR9fF/0dHRzJw5kx9//JGcOXMyceJElEolAwcOTF0dWqVS4eDgwPHjx6lQoYJJy5ckuHYNLlww/HvlCgTdlHj8WEFiBqb6UKkgV24JT08F5cpC2bJQvjxUqwYODiYNWbAetp9kaLVw6xZcvw5BQXD37r+PexKPHylImdpCowH3nHrcc+pxy6Ejm4eO7Ln0OLvpcXKRcHbV4+Ak4egs4eAs4eSiR6UGB+f/vz1qtYSDk8TOlYtp2aMfumQlCfH/b6ZIToLEeAVJiQoSYhXExyqJiVKQEKskPlZBbJSCqHAVYc+URIepiHyhJPS5koS4lH00Qqm8Q4GCX1Ctan+KF3emWDHDh7F0adK0mgjvJy4ujgMHDnDgwAEOnzjM1YtX0SXrUGdXoyylJKl0EhQH8v3n4Q44YbjJmDI1SiSGvDMOCAeeACHAYyAY7K7bob+hJzkiGZVGRblK5WhYuyHe3t40btzYLB2HbZGoj7d7+PAhfn5+rFixAjc3N0JDQ/nv5VutVpM9e3bOnDlDkSJF3qus+/dh2zY4eAiOHoWwUNDYQeFiyRTw1FLQM5kceXVkz60nR24dzm56NPagsZOwd5DQJUN8nBJJDzGRSiJeKAl7qiL8uZLH99Q8uqPm/i0N0ZEK7OyhejVo1AhatoQaNTLW+itYNdtKMkJD4fRpQ0Z95QpcvSZx65aCZC0oVZCvoI7chZLJXVD37yOZPAV15MqvI5uH/u0FGEmSJJP2h0hMUBB86SkXAs+QI29Hnj+25/lDFc8fqXn0tyo1CSlUWKJMaQXlykGFCoYPY7FiJgsj04uKiiIgIIDNWzdz4MABkhKSsKtoR1LdJEOjVF2goJkK/wc4DpwAu2N2JP2VhL2jPd7e3nRs15FOnTrh6upqpsKtk6iPdzNp0iSmTp2a7iReGo2G/Pnzc/r06Qyvp/TwIaxYARs3SVy8oMDZVaJMtSRKV0+kdLUkinhpTX6749kjFdfP2nHtrB03ztrz5B8V+fJLdGivoEcPqFnTtOUJFme9SYZOB5cuwYkTcOYMnDwlcee24cs2X2EdhYolU7CYlsIlkinoaXho7KzyUN6LpDd8EB/c1vAgWM0/wWoe3dFw/5aa5GTwyAE1qhs+jDVqQJ06htszwv8dOXKERYsXsWHTBpL1ydAMdK110BrIK1NQT4AdoNqhgr2gUWnw7ezLgP4DLH5f3dJEfby7iIgIPvjggzcOXdVoNJQuXZrAwMC3JkqSBPv2wZy5sHMHuLjpqdYkgZpNEyhbIwm1xrLX1Pu31JzZ58CpvY78fVNNufISQ4cYEg5xXbNJ1pVk3L0L+/fDvv1wYD+Eh4OTi8QHJbR8WEaLV5UkylRLwi2H6VolbJUuGR7/rSbogh03ztvx93U7/rmtQqk0tHL4+IC3N9SrZ+iImtXo9Xp27tzJN1O+4fyp86hLq0nulQz9gZxyR/eSSGA92K2wI+l4EuUqleOLkV/QvXv3dNfLsUWiPkxj3Lhx/Pzzz6+dCTiFWq2mVq1a7Nu377VD3Pfvh3Hj4fw5KFZWi3eXOBp8FI+dg3V8Jdy5pmH/eicCdzji5Kjg009h1CgQa8vZFHmTDJ0OAgNh0yb4Y6vEwwcKnFwkSldLonytRMrVSqRQsWRxf85IES+UXDllz+UTdlw95cCzx0qcnaFpM+jUEVq1yhof0G3btjFqzCjuBd9D2V6JbqwOqskdlZFOg2q6Cv1WPcW8ivHLjF9o2bKl3FG9F1EfpvHo0SM+/PBDkpKSjNperVbTtm1bAgICUCr/P1vB6dMwdBhcvAA1vBPoNDSGoqUy1pnWkqLClWxf6syeNc442iuYMgUGDAClmIDBFlg+ydDpDBn0pk2wZQu8eAFFSiRTzTuBinUTKV4hSQxzMpEn99VcPmHHmQMOXD1lj1IFTRpDp07QsWPmSziuX7/O8JHDObj/IKouKnTf6sBWF6oNAuUkJfqNepo0a8Jvv/yGl5eX3FFliKgP09q4cSNTp07l1q1bxMTEAKQOc39d4qFSqfjkk0+YO3cu4eEwbhwsWgRlqyfRe1wURbysN7l4WXSEkk3zXdi10pnKlWD+fKhUSe6ohLewXJLx8CGsXg1z5kr8c19B4WLJ1GyeQJ0W8RT0TH/6bMF0YqKUXD5hx/lDDpze74Bep+CjNjBwIDRpYtu9ufV6PTNnzmTCpAlQDrS/aKGO3FGZyFHQjNLAdZj2wzRGjhyZ5lepNRL1YX5Pnjzhxo0b3Lx5kxs3bnD16lWuX7/O06dPAcMS8/b29iQkJCBJEn37Tmbffj/iEvT0GhtFvdbxFo/ZVO7fUrP4WzduXbZjyg/w+ee2ff3K5MybZEgS7NgB/v5w6BBkz6mnfts4GneMJ38RkVjIJS5aQeBORw5tciL4igbPYobOVQMH2l7nqkePHtGpayfOnj2L7hsdfAFktpYwHTAdlN8oqVW7FhvXbiRvXrl6SL6ZqA95RUdHpyYeKY+jRy8TFvaQD0rOwG+ZL9my236fNkkPfyx2Yd2vrng3gbVrDZN+CVbHPEmGTgfr18OUqRLXrymoXD8RH984KjdIELdCrMzfQRoObHTk0BYnHOwUfPYZDB8OOXLIHdnbnT9/npZtWxKeLRztOi2UlzsiM7sImq4acsTnYM/2PSafdOl9ifqwrvrQ6WDoUFi0GHp9GUrVRrfIW6gwCitvCcuI4L80/DTCgzw5Ffy5R0GBAnJHJLzEtEmGJEFAAIz/SuKffxTUaZFAu0+i+aCEaLWwdjGRSnatdGL3ahd0SQpGjIDx4623ZWP37t106NyB5DrJJAck/38ypswuHNSd1GjOaNi6eSs+Pj5yRwSI+rC2+khOhs6dYc+fEqN+jqBqowxMzWljXjxRMWWgB7oENUcOw4cfyh2R8B+mSzIuXoQRI+D4cWjUPp6Og6PJU+jVyWIE65YQp2Dveic2z3PF2Qlm/Kige3fruue5f/9+WrZpia6rDv1CPby6YG3mpgVlPyWazRr27NxDw4YNZQ1H1Id11YckQe8+hkm1Ji0Ko2Ql40aj2LKYKCXf9fNAH6/mxHEFGZyHTDCf908yEhJg7Fj4/XcoUUFL368i8SxrOz2WhfRFhStZ+4sr+zc6UbMGrFoFRYvKHRWcPn2aBo0boO2gRb9cn3XXEdaBsrsS+532BB4OpEqVKrKEIerjX1ZSHwATJsCMGTB+XhgV6iTKFoelRYYqmdQ9J7k9VJw4LtZDsRLvl2TcvAm+vnDnrkTfCZE0aBtvVb94hfd374aGOV+58+KRmoULoUsX+WJ58eIF5SuX53nZ5yRvS856v5hfpgVVKxV5g/Ny+fxlPDw8LFq8qI+XyFwfYJgeoFkzGPxtJE06xb39DzKZpw9UjOmYiz69FMyeLXc0ArD+nX93bNoEVapCPFp+3Pychu1EgpEZFS2lZcq6F9RpE0vXroZJfNJZMsEiuvfqzgvlC5JXii80ADSgW63jqe4pPfr0sHjxoj5eInN9hIdDr15Qu3lClkwwAPIU0jHQL5Lff4ddu+SORoB3bNxcudLQglHvozi+WxUq+l5kchp7iQGTovji13CWLJHo0dPQscySNmzYwN49e9Gu0YIlR76sAhT/ebyuI+wuDBNNvenLdtxL+zLF4k+5IHlVMrt37OaPP/4wwQ6NY5X1EQ7MAxoDHoAjhpVbuwN/pbOvTFQfAN9/D/FJegb6RVq03PTEREXy57oVTO7dmd7VS9G1QlGGNa3NL18M4++g62Ytu07LeOq0jGfESIm3zL4uWECGk4zFi6FPH2jbP4ZPvo60+AI6gnxq+iQwYUEYW7dJdPG1XKIRHx/PyC9HouytNKzOKYe5gATEvPT8HeAjYDzw9C37mPbvPiRMO3dEfUN/gM8+/4yEBPOPIrDa+vgSGA60Ba4DocAS4BJQBfjjpX1kkvoAw7pPv/0OXT6Nxjmb/PNgrPjxWxZ/N5HqTZrhv+sIy09f59Mps7gXdI0vOzbjzP49Zi2/++ho/rmvYMECsxYjGCFDScaZMzB4CHQcHEP30dHmikmwYmWqJzFpURi7dkl8951lyly1ahVPnz1FP1X+i+crJmH4oj0PyLgyuH6ankePHrF27Vqzl2XV9dEPGIFhNVcnoB6wBsMEWmMsF4Yl6wNg9mzIkUeHdxfruU3SuGNXWvUagHvO3Ng7OFKqag1G/TQHvU7HihnmvXjkLqDDu0ssP82UsJ4lQLMmo5OMmBjo3gPK1UzEd7hIMLKykpUM6x58/z0cPGj+8n6b/xtSF0m+ZcDfZDGGZne5+yQUADrCr/N+NXtRVlsfi4D56TxfAcOtkzsYWi0swYL1kZQEK1dBow5xVjPZ4dDvZzL42x9feb6IV2nsHBwIeXAfc69o0bxbHH/fU3D4sFmLEd7C6CRj4kR4Eabn02kRooOnQLOucVRvkkD/Aea973n16lUun7+M/hMr/NUMhi8vK6EfqOfSmUsEBQWZrQyrr4/0xALxQFkMfS8sxBL1AXDgAISFQsN21r8eSWJ8HEkJCRQuXhKFmb9ICnyYTIkKWtasMWsxwlsYlWS8eAHz5kOnodG45zDvxeXM/j109Mqf+nj++CEzRw2me+Xi9K5RGv8xw4mJiuTZowdMHdyb7pWL079uReZO+oL42JdvmENUWCiLv5/I4MbV6VL2A/rWKsuPw/tz78a1NNvpdMkc37WNb/r50r9OBbpWKMqoNo3ZuWIRkj7tMWuTklj36wyGt6jHxxU/pHf1Ukwd3JuzB/ei/3foxca5v6Qew4RubVP/9mLgodTn+9Qs89rjfnzvDjNHDqJ3jdKpz0WFh2XomMyt97goHj1UYM4W4cOHD6N2U5umU95/hQKjAU/AHigIeAPLMHwh2aI6oHZVc+TIEbMVYZP1seHffye8534yygL1ARAYCIU8deTIa54O+Bm9lr3JiT3bAeg0eIRZYn1ZuZqJHA0U90vkZFSSsXEjKFUSjTuY/+pb3bs5m4IeU71JMwCWTfWj3YChLDn+F/2++paj2zbh/8Uwlk75mq4jxrD42CV8h3/O/g1rWD/7pzT7Cn/+lDGdWnBi93YGTp7KijPX+XblJmIiIhjftTU3L51P3fZi4CF+Hj2YcjXr4r/7KAsOn8fHtwdLp/mx8qcf0ux30XdfsXPlYgZM+oHlp67jv/soBT4sxrShfbhx/jQAnYaMZFPQY+wdndL8baV6jdgU9BjPMmkXdnj5uOd9PYbm3Xqz4Mh5pq3fgfLfdtCMHJO55S6go2qjBFasMF8ZRwKPINWVTNsxLwSoBqwF/IEXGPpUNAT6kn6Tuy1QA3XgaOBRsxVhc/XxFMPtrAGAped4sUB9AJw4KVGsgvkm3crotex1IkKfs+qnKXh37kbtFh+ZI9RXlKyURPAtBWFhFilOSIdRScahw4YOf/aOls8Im3T6GM8y5bF3dKJB204UKlaSC0cP0qbPIIqWKoODkzNNfXuSu2Bhzh85kOZvV82cyvPHD+kz3o/KDZrg4ORMoWIlGT1rLkgSi79L+9OmTPXadBg4HJdsbmTL7kHLHv2o17o9O1YsIj7m//1QLp88RqFiJahQuz52Dg6458hFrzGTyF/EdJPmt/tkGGWq18bewZHiFSqz4doDsmX3yPAxmVvlhomcOInZbpncuncLXSkT/0IbD9zD8IXWGkOHzTzARKC5aYuytGSvZG7du2W2/dtUfYT++/cNMQxtlYG56wPg77+hQFHrnkYgOiKc7wd0o0yNWgzym26xcvMX1SFJ8M8/FitSeIlRSUZwsERBT3kGHHuWTbuyoUduw6T0nuXSPp8jT17Cn6UdQ3jmwB4USiVVG3qned49Z24KFSvJnWuXCQ15AkDVhj58u2LjK+UX8SqNLlnLP7f/f6GoVK8RNy+eY97XX3Lrr/Opt0hm7zlGmeqmGdNXvHyldJ/PyDFZQqFiWuLj4NEj8+z/xYsXkNPEO93y778t0nltNzDSxOVZUi549vyZ2XZvM/URCzQDSgOrkW+5eTPXB0BYqAJXd+vtI5MYH8d3/T+moGcJRs74PbVV1hJS3pcXLyxWpPASo/rEx8WBgwytGABOLmnHBSqUSpQqFfYOaXvcKZUq9P/pO6FNSiIuOgqAHlVLvnb/T+7fJUfefMRFR7Ft6XxO79tN6NPHxEZFpdkuKf7/t4o++XoKJStW4dAfAfj1NrTBlqpag6a+Panhk96VMuMcXmqahIwfkyU4OBnOizgzjZyLj4sHZxPuMBGIBByQdcip2ThDbEys2XZvE/WRDHTGMMJjOfIlGGD2+gCIjwc7B+vsd6DTJfPTiIF45MnL8On+Fk0wgNTW91jzVoHwBkYlGR4eCiLDbGvlI42dHc7ZspEQG8fay3dRqd58qFOG9ObGudP0m/Ad9Vq1wzW7BwqFgh3LF7J06uQ0w60UCgUN2naiQdtO6JK1XD1zkq2L5/Lj8P70GTuZNn0HpW6rVCpJTudeQmx01CvPmfqYLCEy1HBe5DDTrI/ZPbITHhpuuh3aY1iGPBKIJvMlGqHgkcN8a2bYRH0MwpC8bCHtFa4YhhlDTd1p9U3MXB8Abu4QE2n+YTPvci2b9/UYtElJjPltSZrr1bCmtRkxYzYlKph3IbmU90WGZWSEfxmVOVSqCHeu2pk5FNOr4dMSnS6ZoAtnX3lty8LfGdSoKjpdMnqdjqALZ3HPmZtWPfuTzSNH6vCqpMRXZ+zrWc2LR3dvA6BSa6hQuz7jfl+KQqF4pV9I9ly5CXua9vZFxItnPH/8bvcXjD0mSwm+bEfuPJhtaeVcOXOBqVub2//7b3prG1QCRpm4PEt6CnlymW+da6uvDz/gGrAVQwIjNzPXB0DOnBJR4eZvIcjotWz9bzN5cPsW4+YsRWMnz/dHVJjhfclp6lt8gtGMSjJatoTgyxqePrCSmV6M1GP0V+QtXITfvxrNhaMHiYuOIiYygr3rV7Lh95/pPeZrVCo1SpWKstVrE/HiGVsXzyEqPIykhASunj7On2vTHzoxf/JY7t+8gTYpicjQF/yx6HckSaJczTpptqtQpwFhz56ye/VSEuJiCfnnbxb/8DVu7/jT39hjspSTux1p09p8+69avip250x8gZoKFMXw5bUTwy/oh8BQ4Ak2nWTYnbWjaoWqZtu/VdfHMuAb4DSGFhHFS487pgzaOOauD4CyZRX8fUNj1jIgY9eyQ1vWE/DbTIL/ukD3ysXTDM/v6JWfkH/+Nnu8AHevq7F3AE9PixQnpMOoJKNpUyhYCP5Y+LrVoUzn1l/n6eiVnzMH/gSga4WirPllOneu/kVHr/xcOnYYvU5HR6/8bFnwGzfOn6GjV36unT1JYnwcHb3ys/63mQC45cjJ9A27qO7dnEXfTaBPrXIMb1GPU3t3MW7OsjTDqEbPmkdT357sWrWEAfUqMaRJDQ7/sZF6bQw/s77p58uYjoau7t+t3EyBD4vx8+jB9K5eis9a1udi4GGGfPcTHQZ9luZ4uo0ci3fnbmya9yt9a5fjt/Ejadd/CNlz5iY6IpyOXvlZNfOHdI+7o1f+V96fjByTuV06Zs+d62oGDjRfGfXr10d7QfvqmiHvIy9wFvDFsNZFDqA6hgW2AoHCGdjXDv7/JfYIw/TVKf9fZLqQjRIJ2sta6tWrZ7YirLo+Xu23LS8L1AdAndpw65LG7NNnG3stAzj5507zBmOkmxftqFIFZGpIEQCFZOTcritWQN++8PWSUMrVTDJ3XIKVi4tW8GWHXNSorMKci00+f/6cfAXyoVusg57mK+e1VmEody4w2IT7VQNVgVMm3OdS0AzREPI4BA8z3YQW9ZEBFqgPgMuXoUIF+GFNKF6VxbU5hU4HQxrn4dPBSvz85I4my1pvdG/OXr2giy/4f5GdiFDb6gQqmN7Cb93RJ6qYZ+b5B3LlykW7du3QzDV/c7Ct08zR0LlzZ7N+oYn6MJ4l6gOgfHmoUFHi4CYrmuPeClw44kDYMyU95UiGhVQZyhbmzgFXZwU/DvUgJkokGlnVqp9dOb7LgTVrIK8FFsn6dOinaE9qwbwTJ77ZEAy3QN7njuE4/n8rxdRzJx0A7Tktw4YMM/GOXyXqwwgWrA+ATwYoOL7LkfDn4rqcYucKZxo3Fv0x5Gb07ZIUf/8NDRtJqBx1TFoSSrbs1jsJjGBakgTLp2dj10pnliwxtG5ZSrOWzTj0zyG0l7Tyr3hqbXSgqaSh2YfN2P7HdosUKerjDWSoj4QEKFESvGrGMfjbSIuUac0uHLHnh0EeBAZC3bpyR5OlGX+7JEWRInD4kAJdvIpJ3XPyd5BoNs0K4mIU/PJFdnavdmbtWssmGAC/zvoVKViCXyxbrk2YAdyCWT/NsliRoj7eQIb6cHCAH76HA5ucuH0la1+TkxIUrJiRjbbtRIJhDd6pba1IEThxXMGHhVSM983B7tWvzk4pZB7BlzV82T4Xt845sGc3dO5s+RhKlizJ999+j/IrJRyzfPlW6wgoJymZNmUaxYoVs1ixoj5eQ6b6AOjeHRo3gl8+z05cjAXXtLcyy6ZlI+q5ml8sl+MJb5Dh2yX/pdPB99/Dd99BxTqJ9PkqivxFLDcRlGBeCXEKNs1zYdtSFxo3MowwMtekW8aQJIk27dqw99xetMe0hrkVsrLboKmnoVWdVmzesDl1AjlLEfXxEpnrA+DJE0NH0JLVEhg1MxxFFuuicXS7I7+OcScgADp1kjsaAVj/XklGimPHYOhQiaAgBS17xdJpSDROLtY5l77wdpIEgdsdWf1zNrQJSr7/DoYNAxmuma+IjIykfuP63Ai/gfaIFgrJHZFM7oOmvoYyecpwZP8RsmXLJksYoj7+ZSX1AXDoEDRvAT5dYuk3IePLF9iqS8fsmTbEg5Ej4ccf5Y5G+JdpkgwwtGrMnw+TJoGk1NOqdyzNPo4VyYYNkfRw9qADmxe4cPeahgEDDC1V1jYlb2hoKHUb1eVO3B20O7Xw+rXiMqfroGmtoYRbCQIPBpI9e3ZZwxH1YV31AbBxI3TtCh/1i6H76Gir+IFgTpdP2DP90+x07qRg+TLr+EEkAO/S8fN1VCoYOhSCg2FgfyVbF7oypHEe1sxyTV1ES7BOumQ4/Icjoz/KxYzPslPmQw3nzsG8edaXYADkyJGDI/uPUCFXBdS11LBf7ogsaA+oa6uplL8Sh/cdtoovNFEf1lUfYLhVsHQpbF/mwu/j3bHgckYWd3SbI1MGedChvYLFi0SCYW1M1pLxsuhoWLIEpk6DsDCo1jiBBm3jqVw/AaVtLYGSaT25r+bodkcOb3biRYiSDh3hGz8oXVruyIyTkJBAn3592LBhA/oJepgAZNaO9VrgG1BMU9CtWzcWL1yMvb01rAD2f6I+rKs+APbuhY4dobCXlhEzwsmZz9QTgshHlwyrf87GtqXOfPEFTJ8uEgwrZLrbJa8TFwdr18KiRXDqFOQrrKNh+zjqtoonb+HMc8LbirhoBWcPOnBosxNXz9iRL79E/34KBgyAwhlZs8NKSJLE7Nmz+XLcl0hlJLTLtFBG7qhM7Aqo+6hRBamY9dMshgwZIndEryXqw/pcvQpdusCjJxKDvougps+rK0vbmpB/1Ph/4c7DOxrm/A69e8sdkfAa5k8y/uvaNVi82DBKITQUPiyVTHXveGo0TaBw8UzcniezqDAlZw44cGafA5dP2iMBrVvDgP7QvLnhVpetCwoKolvvbvx14S+kwRKSn2RYaMuWvQDF1woUCxVUrlqZ1ctXU6JECbmjMoqoD+sSFwcjRhh+7FVvkkjfryLJXcD2fuRpExVsXujCHwud8fKC9esUeHnJHZXwBpZNMlIkJ8Phw7B5M2zeAk9DoEARHeXrJFC+dhJlqyfi5Co6jL4rnQ5uX7bj8gk7rpy058ZFO+zsoFkz6NAe2rQBK7l1bFJ6vZ4lS5YwduJYopKiSB6VDMMA8y4dYXqhwGxQ+6txd3Tnxx9+pHfv3iiVttW3SdSH9Tl4EIYOg/v3JVr2jOWjfrG4ulv/rM06nWHE28Y5rkSHqfDzg88+A01mvR2XeciTZPyXXm+4jbJ1K+zdB5f/AhRQopyWMjUT8aqcRIkKWlzcrP+DIBdtkoJ7N9QE/2XHlVP2XDtjR1yMggIFJXy8FbRqBS1agLOz3JFaRlRUFD/99BO//PYL8dp4kgcmG1bsLC53ZG9xC5gLqoUqnByc+Hz453z++ee4uLzPAh3yE/VhXZKSYPZsmDYN4hIkmnWLpUW3OHLktb6WjaQEBcd2OrJlgQtPH6no3cswL1P+/HJHJhhJ/iTjZaGhhmz7wAHYf0Dizm1DT54CRXQUK59EsfJaipVLolCxZBydrSp0i9Dp4Mnfau5e0xB8WcP1s8E8ulsCrdaJ7NmhQUPw8YYmTaBkVhtK+JLo6GgWLFjAjF9m8PTRU9R11ST3TYb2gLvc0f0rAtgMmqUatMe15CuUjzGjxjBgwACb/zJ7magP6xIbC3PmwIyfDNfdqg0T8e4cS8W6iahkXo/mn2A1+zc4cXSrEwnxCnp0h4kT4cMP5Y1LyDDrSzJe9vw5nDkDp0///xEZaehFnKegjoLFkilUXEvh4skU9Ewmd0EdLtlsv9VDm6Tg+SMVj+6peXBbzT831Ty6q+HBbTVaLWjsoEIFiXt3K6DXh/Hll98xZkxvVCrba8I1N51Ox59//smSZUvYtm0bOr0OZX0lyR8lQ3PA0rfVb2IY+rhVjf6YHpVKRft27enXpx8+Pj422QyfEVZbH9vU6AOzXn0kJcGWLTBp0mxu3+6Nq5srVRslUKNpAmVrJOLgZP6vCL0O7l7XcGa/A2f2O/LgjoqiH0oMGqigb1/IndvsIQjmYf1JxsskCe7ehStX4Pp1w79XrkrcuqVAm2TYxiWbRJ6COnIVTCZ3AR25CujwyK3DLYcetxx6PHLrLPLBeR1tkoKoMCXhz5VEhCqJfKHi+WMVzx6peP5QzdOHKsKeKUmpmcIfSJQto6BcOShT5v8Pe3vDREgzZsxg1qxZeHp68s0339BZjsVFbER4eDi7d+/mj21/sGvPLmIjY9Hk1qCrpUNfRw/lAS/gAxMUJgH/AEHAJVCeUKI6qUL7XIudgx25cuZixo8zaNmyJW5ubiYo0PZYS324uLvQqkUr2n3UjhYtWmS5+vj+++/x8/Nj8eKdPH/ejE2b4cxpUCjBs4yWUlWSKFY+icLFk8lXJPm9O4uHPVXx4LaaO9c0BJ23I+i8HbExCgp/INGpo4IOHaBWLcjk+V1WYHtJxutotXDvniEBuXfPsCT9vXtw567EP//Ai+dpB1A7OEq459Tj6ibhlE2Pg5MeB2cJR2cJR2c9ztkMb4uDkx71fzoXOf+nlSQxXkGy9v/7jY0yJAYJcQriYxWGf2OUxEUrSYhVEBejJOKFkqiItLE4OUPhwhIffqigaBEoWvT/D09PMGaG4ps3bzJp0iQ2bNiAt7c3M2bMoGLFihl9G7MUrVbLuXPnOHnyJIHHAzl26hgvHr8AQOWiQl1cTXL+ZHR5dFAAcAVSvntSOs6G//tvJBAFPALVMxXqR2qSbyejizHc585VIBd1a9alXp161KpVC51OR+PGjfHz82P8+PEWO2ZrJmd9VK1aFbU6a65Zv2rVKnr16oW/vz/Dhw9Pff7pUzhyBI4ehUOHJW4GKdDpDJ0tCxRNJkc+HW45deTIq8fZxXD9VKnAwVlPcpKCxAQF2iQFcdEKIl4oCXuqIuK5isd/q4mONFwD8+WXqF9PQf360KCB4ceTkKlkniTjbbRaw62Xp08Niwg9f274NyICwsMNk4dFRUNUFERHS0REGP4uMlKB9G9ekZi4i/j4G8DnANg7gKPD/8twcZVQq8HJyZAYZHNV4OYG7u7g6mp4Lk8eyJcPcuWCvHkNDycTLmJ76tQpvvjiC06ePEnHjh358ccfKVKkiOkKyOTCw8O5ceMG169f5/bt2zx58oRHTx/x4PEDYmJiiIowrAUREx4DgEt2w336bO7ZcHFxoXCBwhTIU4B8+fLh6elJmTJlKFWqFO7u7q+U5e/vz+jRo9m9ezdNmza12DHakrfVR0RYBDqtjsS4ROD96iMr2rVrF23btmX8+PF8++23b9w2IQFu3DDMu3HzJjx8aLiGPngoER0NMdEKtFpDXw+NHTg7gb2DhKsr5MmjoFBBw/WueHFDMlGuHHjY2kgjIaOyTpJhCuPHj2fv3r2cP39e7lDeSJIkNm7cyLhx4wgJCWH48OGMHz8+yzUB24JevXqxa9cuzp07J5LBd7B69Wr69u1LYmKiLKue2rKzZ8/SuHFj2rVrx4oVK8T7J5iD6dYuyQqio6NxdXWVO4y3UigUdO7cmRs3bjBr1iyWLFmCp6cn06dPJykpSe7whP+YO3cuefPmpWvXriQmJsodjs0JCwvDw8NDfEFm0J07d2jTpg3169dn6dKl4v0TzEYkGRlgK0lGCjs7OwYOHEhQUBADBgxg8uTJlC9fng0bNsgdmvAvZ2dnNm/ezI0bN/j888/lDsfmhIWFkSOHrU8lalnPnz+nRYsWFC5cmPXr12fZviiCZYgkIwOio6Ntcqy8h4cH06ZN49atW1SvXh1fX19q1arF8ePH5Q5NAEqUKMGKFSuYM2cOy5cvlzscmxIeHo6HuLFvtOjoaJo3b45er2f79u02eT0TbItIMjIgJibGployXla4cGFWrFjB6dOnsbOzo169enTp0oW7d+/KHVqW17ZtW0aNGsWQIUO4ePGi3OHYjJTbJcLbabVaOnfuzIMHD9i9ezd58uSROyQhCxBJRgbY2u2S16lWrRpHjhxh7969XL9+nVKlSjFo0CCeP38ud2hZ2vTp06lWrRq+vr5ERkbKHY5NCA0NFUmGESRJ4pNPPuHEiRPs2bOH4sWtfU53IbMQSUYGZJYkI4W3tzcXL17kp59+YvPmzXh5eTFr1izRAVEmarWagIAAYmNj6dWrF2Lg19uJlgzjjBkzhjVr1rBhwwYqV64sdzhCFiKSjAyw1T4Zb6LRaBg+fDi3b99m4MCBTJgwAS8vL9asWYNeb/vTs9uaPHnysHHjRvbs2cNPP/0kdzhWLywsjOyZcUlhE5ozZw4zZ85k4cKFNGvWTO5whCxGJBkZkNlaMv7Lzc2NqVOnEhwcTNOmTenVqxfly5dnx44dcoeW5dSqVYupU6cyfvx49u3bJ3c4Vk20ZLzZ+vXrGT58ODNmzKB3795yhyNkQSLJyABb7/hpjAIFCjB//nyuXLlC6dKladOmDT4+PqIzooWNHj2aDh060KNHDx49eiR3OFZJkiQiIiJEkvEahw8fpnfv3gwePFgMjxZkI5IMIyUkJKDVajN9kpGiVKlSBAQEcOLECeLj46lSpYoYiWJhS5YsIWfOnHTq1ElMopaOyMhIkpOTRZKRjqtXr9K+fXvatWvH7Nmz5Q5HyMJEkmGkmJh/16rIZH0y3qZWrVoEBgayfv16Ll68mDoS5dmzZ3KHlum5uLiwefNmrl+/zpgxY+QOx+qEhYUBiCTjJQ8fPqRly5ZUrFiR5cuXZ/ql6gXrJs4+I0VHRwNkmZaM/0qZpvz69evMnj2bbdu24eXlxfTp04mPj5c7vEytZMmSLFiwAH9/f1auXCl3OFZFJBmvCg0NpWnTpri5ubFlyxbs7e3lDknI4kSSYaSsnGSk0Gg0DBw4kNu3bzN27Fh++OEHSpQowYIFC9DpdHKHl2n5+voyYsQIhgwZwrVr1+QOx2qkJBliWnGD+Ph42rZtS3R0NLt27RIrzQpWQSQZRhJJxv85OzszduxY7ty5Q4cOHRg2bJhYE8XMfvrpJypXrkyHDh2IioqSOxyrEBYWhlqtJlu2bHKHIjudTkePHj0ICgpi3759FCpUSO6QBAEQSYbRUpKMrNYn401y5cqFv78/V69epUyZMvj6+lKnTh2xJooZpEzUFR0dLSbq+lfKHBliBVEYOXIku3fvZuvWrXh5eckdjiCkEkmGkaKjo1EoFDg7O8sditUpWbIkAQEBnDx5ErVanbomyu3bt+UOLVPJmzcva9asYefOncyaNUvucGQn5sgw+Oabb5g7dy6rVq2iTp06cocjCGmIJMNI0dHRODk5oVKp5A7FatWoUSN1TZSgoCBKly7NoEGDePr0qdyhZRoNGzZkypQpjB07liNHjsgdjqxEkgGLFi3Cz8+PX375hQ4dOsgdjiC8QiQZRsoKE3GZire3NxcuXODXX39l27ZtlCxZkilTphAbGyt3aJnCF198Qbt27fD19eXx48dyhyObrJ5kbN++nSFDhjB58mQ+/fRTucMRhHSJJMNImXlKcXNQq9UMHjyY4OBgRo8ezbRp0yhevDhz585Fq9XKHZ5NUygULF68GDc3N7p27Zpl38+snGQcP36crl270rdvX/z8/OQORxBeSyQZRhJJxrtxcXHh66+/5t69e/Tq1YvRo0dTvHhxMez1PWXLlo2tW7fy119/Zdkpo7NqknH16lU++ugjvL29mTNnjtzhCMIbiSTDSJlxBVZLypEjB9OmTePWrVs0a9aMoUOHUqFCBTHs9T14eXmxfPlyfvvtN5YuXSp3OBaXFVdgffDgAS1btqR8+fKsX78etVotd0iC8EYiyTCSaMkwjUKFCqVZgC1l2OvRo0flDs0mtWvXji+//JKhQ4dy7tw5ucOxqKzWkvH8+XN8fHxwd3dny5YtODg4yB2SILyVSDKMFBsbK1oyTChlAbaTJ09ib29PgwYN8PHx4a+//pI7NJszdepUGjZsSMeOHXn+/Lnc4VhMeHh4lkkyoqOjadGiBcnJyezdu1fM5inYDJFkGCk+Ph5HR8cM/92qVatQKBSpj/QSlUuXLtGqVSvc3d1xdXXF29s73Qmtxo0bl2ZfNWvWfKdjsSY1atTg4MGD7Nu3j9DQUCpXrmzTq73KUd9KpZLVq1ejVqv5+OOPSU5ONvlxpceYYwXYtWsXJUqUeGPTfkbP7ejoaJKSkjKUZLwp3vDwcObNm0fjxo3x8PDA0dGR4sWL071793QTX0t+FpOSkujUqRMPHjxg9+7d5M2b1+i/lbOOBAFEkmG0hISE92qenDt3LpIkpa7mmuL06dPUrl0bV1dXbty4wb179/jwww9p2LAhe/fuTbPttGnTkCQJSZIy3Xwd3t7enD9/nnXr1qVZ7dVW59iwdH17eHiwefNmTp48yYQJE0x+PG/yumO9c+cOH330EePHj39rPWb03H6fdUvSi/fLL79k+PDhtG3bluvXrxMaGsqSJUu4dOkSVapU4Y8//niveN9VynThp06dYs+ePRQvXvyd9iNHHQkCiCTDaO/akvEmer2e/v374+7uztKlS8mXLx85c+Zk7ty5eHp6MmDAABITE01apjV7ebXX7du3U6xYMcaNG5cp1uswd31XqFCBBQsWMGPGDAICAkwY+buZNGkStWvX5vz58ybvz2SOFVj79evHiBEjyJs3L05OTtSrV481a9ag0+kYM2aMycrJiFGjRrFjxw62b99OpUqVTL5/c9aRIIBIMoz2vi0Z6Tl69CjXrl2jU6dOaRIYlUrFxx9/zIMHD9ixY4dJy7QFKau9BgcHM3HiRObPn4+npyfTp08nISFB7vDemSXqu3v37gwbNoz+/ftz9erV9w35vSxevJhx48aZZQSEqZOMRYsWMX/+/Feer1ChAo6Ojty5c8fi68VMnDiROXPmsGrVKurXr2+WMsxZR4IAIskwmjmSjIMHDwJQtWrVV15Lee7AgQMmLdOW/He11/79+/PNN99QsmRJm51jw1L1/fPPP6eu2BoREfHe+3tXpm75+6+wsDCUSiVubm5mKwMMHb7j4+MpW7asRRdimzNnDlOmTGH+/PlmnS7cnHUkCCCSDKPFx8enJhlBQUG0a9cONzc3nJycqF69Ojt27MDb2zu1U9SAAQPeus+goCAAChYs+MprBQoUAODWrVsmPArb5OHhkTrHRvPmzWVZWj40NJTRo0fj6emJvb09BQsWxNvbm2XLlhEfH2/UPixV3xqNhoCAAOLi4ujZsyd6vT5Df2+KYzW3sLAw3N3dUalUZo035RyzZD+XtWvXMnz4cKZPn07//v1fed1U1x9BsASRZBgpISEBR0dHbt++Ta1atTh37hwbN27k2bNnLF26FH9/fy5fvoy9vT2SJLFo0aK37jPlV2Z6K7um9AIPDw836XHYsoIFCzJ//nwuX76Ml5cXvr6+1KtXz+xLy4eEhFCtWjXWrl2Lv78/L1684Pz58zRs2JC+ffum28yeHkvWd548edi4cSP79u3ju+++M/rvTHWs5hYaGoqHh4dZ43369Cnjxo1jwIABdOnSxYTRv97+/fvp27cvw4YN48svv3zldVNefwTBEkSSYaSUloyvvvqKiIgI/P398fHxwcXFhTJlyrBmzRqTLgCWcv/Xkk20tqJUqVJs2rSJU6dOYWdnR926dfHx8THbZFTjx4/n3r17+Pv707p1a1xdXcmTJw8TJ06kefPmJinDHPVds2ZNfvnlF7799lu2bdtm1N9Y4lhNIWWODHPFGxoaSvPmzWnYsCHz5s0zYeSvd+LECdq1a0fXrl3x9/dPdxtLXX8EwVREbx8jpbRk7NmzB4BmzZqleT1Xrlx4eXlx7do1o/eZMqFOeheHlOfEpDuvV716dQ4cOMCxY8eYMGEC1apVw9vbmxkzZlCxYkWTlbNlyxYAWrRo8cpru3fvNno/ctT34MGDuXDhAj169ODkyZOUKVPmjdub6ljNLWW2T3PEGxsbS7NmzShdujQrVqywyHDNK1eu0KZNG5o0acKiRYtem2ya8vojCJYgWjKMoNfrSUpKQqVSER0djYODQ7qT2mR0HQUvLy8AHj58+Mprjx49AqBEiRLvEHHWUrduXY4cOcK+ffsICwujSpUqdOnSxST9GxITE4mMjMTBweG9h/jJVd+///47FStWfGtHUFMeq7ml9MkwdbzJycl07tyZAgUKsHz5coskGLdv36Zp06ZUrFjxjeuRJCYmmvT6IwiWIJIMI6QMm3R1dcXV1ZWEhIRXJrUBePbsWYb226hRIwDOnz//ymspzzVp0iSj4WZZ3t7enDt3jj/++IObN29SpkwZevXq9V6zh9rb2+Pm5kZCQgLR0dHvFZ9c9f3fjqC+vr6vHZljymM1t7CwMHLmzGnyeAcNGkRiYiIBAQFpvuyLFSvGqVOnTFLGfz169AgfHx8KFy7MH3/88cYRbPb29ia9/giCJYgkwwgpPdQdHR1Tm2VTmi1ThISEZPiXc4MGDShdujQbN25MM/+DTqdj3bp1FCpUiFatWr1n9FmLQqGgTZs2XLx4kTVr1nDy5MnU2UMfP378Tvts3749YJh6+WWVKlVi1KhRRu1HzvrOmzcvW7duJTAwkEmTJr12O1Mdq7ml3C4xZbx+fn5cu3aNrVu3Ym9vb7JYX+fFixep/Sp27dplVGuMKa8/gmAJIskwQsoXgoODA1OmTMHDw4ORI0eyb98+YmJiuHr1Kn379s3QmgJgWHNi8eLFhIWF0bdvX0JCQggNDWXYsGEEBwezcOFCsdLiO1IqlWlmD925cyfFixdnxIgRGZ6qfOrUqRQtWpRRo0axc+dOoqOjefjwIUOHDuXJkydGf5HJXd+VK1dm/vz5TJs2jXXr1qW7jamO1dxSlnk3VbzLli3jm2++4fTp07i6uqZZo0OhUHDnzh2Txh8VFUXz5s1JSkpi7969Rk+PbsrrjyBYgkgyjPDflgxPT09OnjxJtWrV6NSpE3ny5GHQoEGMHz+eYsWKZXjfNWvW5MSJE0RGRlKyZEmKFClCcHAwhw8ffqVzl5BxKbOH3r17l1mzZhEQEJA6VbmxE1XlzZuXs2fP4uvry/Dhw8mRIwfVq1cnPDycwMBAChcubHQ8ctd3z549+fTTT+nXr1+6t21Meaw7duxI/ZJ+9OgROp0u9f/vO8QyLCyMHDlymCzejRs3vlc8GREfH0+bNm148uQJ+/btI1++fEb/ramvP+asI0EAMbrEKCnrSaQ0oZYoUSK1V7spVKpUKd3mXsF07OzsGDhwIN27d+e3335j+vTpLFy4kOHDhzN69GiyZcv2xr/PkSMHs2bNYtasWe8di9z1/fPPP3P16lU6duzI2bNnyZUrV5rXTXWsrVu3NstU3HFxcSQkJKROKW6KeC01fb9Wq6Vz585cu3aNI0eOULRo0Qzvw5TXH3PVkSCkEC0ZRng5yRBsV8pU5ffv32fMmDH88ssvqeuiWMtsluamVqvZuHEjKpWKDh06oNVq5Q4pQ8yxOJol6PV6evXqxeHDh9m2bdtbhxMLQmYgkgwjJCUlAYZfw+9qyJAhKBSKdIeeGWvcuHGpTZm2uHaHNXF1dX1lXZQSJUrg7+9vkpVvrb2+U5aGv3jxYrozS2aEpY/1fZMMOepGkiSGDh3K5s2b2bx5M7Vr137nst+FtZ+PQiYmCW915MgRCZCePHmS7utr166VgDSP/v37WzhK4X08e/ZMGjt2rOTg4CB98MEH0vz586Xk5GS5wzK7TZs2SQqFQlq0aJHcoRjt0KFDEiA9e/ZM7lCMNnbsWEmlUkkbN240+b7F9UewYutEkmGEffv2SYAUGhoqdyiCmd27d0/q27evpFarpdKlS0sbNmyQ9Hq93GGZVUpydfr0ablDMcrGjRslhUIhabVauUMxytSpUyWlUimtXLlS7lAEwdLWidslRjDF7RLBNhQpUoQlS5Zw7do1ypcvj6+vLxUrVmTTpk2ZtoPcDz/8QKNGjWjfvn3qzKPWLCwsjGzZsr12Zkxr8ttvv/HVV1/x66+/0qNHD7nDEQSLE0mGEUSSkfWUKFGCtWvXcuXKFSpUqICvry/lypVjxYoVme5+tEqlYv369Xh4eNCmTRurX2grZSIua7ds2TJGjBjBlClTGDZsmNzhCIIsRJJhhJQkQ6PRyByJYGkpi2RdvnyZypUr069fPypUqJDpkg1XV1e2bdvGw4cP6dWrF3q9Xu6QXitlBVZrtmHDBgYMGMDXX3/NuHHj5A5HEGQjkgwjJCUlodFoxLLrWVhWSDaKFi3K5s2b2blzJ35+fnKH81rW3pKxZcsWunXrxvDhw5k8ebLc4QiCrESSYYSkpCRxq0QAMn+yUbduXebNm8f333/P6tWr5Q4nXdacZPz55598/PHHDB482CQTtwmCrRNJhhFEkiG8LDMnG3369GHUqFEMGDDALCuPvi9rTTL2799Pu3bt6NatG/7+/nKHIwhWQSQZRtBqtSLJENKVkmz89ddflC1blr59+1KpUiU2btxo1f0a3mbGjBn4+PjQvn17/vnnH7nDScMak4wTJ07Qvn172rRpw8KFC1EqxaVVEEAkGUYRLRnC25QpU4Z169Zx5coVKlasSNeuXVNHoyQnJ8sdXoYplUpWr15Nrly5aNu2LTExMXKHlMrakozTp0/TvHlzmjZtypo1a1CpVHKHJAhWQyQZRhBJhmCs/95GqVSpEv369aNMmTIsX77c5pKNlBEnjx8/pmfPnlbTMmNNScbFixdp3rw5DRs2ZN26dTYxd4cgWJJIMowgkgwho0qXLs2qVasIDg6mefPmDBo0iGLFiuHv709CQoLc4RmtSJEibN68md27dzNx4kSLl3/69GmCgoJ49uwZycnJJCYmEhsbaxVJxuXLl/Hx8aF69eoEBASIIe6CkA6FlFmnMXxHf/75J76+vqjVahwcHADDKqwKhYI8efKkbpctWzbat2/PF198IVeogg25f/8+P//8MwsWLMDNzY1Ro0bx2Wef4ejoKHdoRlmxYgW9e/dm4cKFDBgwwGLl1qhRgzNnzqT+38nJCb1eT4ECBShcuDC5c+fGw8MDDw8Pxo8fj7Ozs0XiCgoKolGjRhQvXpzdu3dbrFxBsDHrxdolL4mPj5ccHR1fWXAovce2bdvkDlewMSEhIdLYsWMlJycnKVeuXNLkyZOlyMhIucMyypgxYyQ7Ozvp8OHDFitz0qRJklqtfu1nUKFQSAqFQqpRo4bFYgoKCpLy5csn1alTR4qOjrZYuYJgg8TaJS9zcHCgXbt2b236zJ49O82bN7dQVEJmkSdPHqZNm8bff//N0KFDmTVrFp6envj5+RERESF3eG80depUWrRoQadOnbhz506625i634a3t/cb+7JI/zbEjho1yqTlvk5wcDCNGzemaNGi7N69+72WTheErEAkGeno3LnzGy9sdnZ29O7dW9yDFd5Zrly58PPz486dOwwbNgx/f38KFy7MuHHjCAsLkzu8dCmVSlatWkWBAgX46KOPiIyMTPN6SEgIjRo1Ijw83GRl1q5dGycnpzdukzNnTjp06GCS8rRa7Wtfu337No0aNeKDDz5gz549uLq6mqRMQcjMRJKRjhYtWrzxXnlSUhK9evWyYERCZpUzZ078/Pz4559/mDBhAgsXLuSDDz5gxIgRVrkiqouLC1u3biU0NJSPP/44deKxq1evUrVqVY4ePcqqVatMVp5araZx48avHRaq0WgYOXKkyRL+oUOHsnz58leev3//Pj4+PuTJk4edO3eKBEMQjCX3DRtr1aVLF0mj0aR7H7hEiRJyhydkUpGRkdK0adOkPHnySA4ODtKQIUOku3fvyh3WK86ePSs5OjpKX375pfTnn39KLi4ukkajkRQKhVSqVCmTljV79mxJpVKl+1nUaDTS06dPTVLO/fv3JZVKJSkUCmn58uVpni9atKhUqVIlKTQ01CRlCUIWsU4kGa+xceNGSaFQvHJRU6vV0k8//SR3eEIml5iYKC1fvlwqXry4pFQqpdatW0vnz5+XO6w0Vq5cKSkUCkmlUklKpTLN5+Ts2bMmKycoKOi1CUa/fv1MVs6wYcNSf1golUpp+fLl0j///CN9+OGHUoUKFaQXL16YrCxByCJEx8/XadGiReoQ1v/S6/V069ZNhoiErMTOzo5evXoRFBTEunXruHfvHlWrVqVNmzacPHlS7vCQJIng4GAkSUKn06Xp8KnRaFi0aJHJyipZsiQFChR45XmtVstnn31mkjJCQkJYuHBhap8MvV5Pnz59qFKlCq6urhw4cIAcOXKYpCxByEpEkvEaTk5OtGrVKs0MfiqViiZNmpAvXz4ZIxOyEqVSSefOnbly5Qpbt27lxYsX1K5dm7p167J9+/bU0RWWlJCQQNeuXfn+++/TfV2r1bJixQqTTkXesmXLNBPiqVQq6tSpQ4UKFUyy/59++umV91KSJF68eMEnn3wiEgxBeEciyXiDLl26pFlRU6/X079/fxkjErIqhUKR2ooRGBhI9uzZ+eijj6hcubJFV34NCQmhTp06bNmy5Y3DVRMTE9mwYYPJyvXx8Ukz8kOv1zN69GiT7Ds0NJQ5c+akO7JEkiQ+++wzVqxYYZKyBCGrEUnGG7Rq1Qp7e/vU/zs6OvLRRx/JGJEgkNqKcfHiRcqVK0e/fv0oWbIk/v7+JCYmmrXsq1ev8vTpU6Pmw5g3b57JyvX29kahUKT+P2/evCb7LP7yyy9vHLqq1+vp27evSDQE4R2IJOMNnJycaNmyJWq1Go1GQ7du3WxmGmgh86tYsSIrVqzg5s2bNGnShC+//JISJUrg7+9PXFycWcr09vbm7t27zJw5Eycnp9cOHdXr9Zw5c4br16+bpNzs2bOn3hpRq9WMGjXKJIuRRUZG8ssvv7x18TpJkujbty979ux57zIFISsRScZbpNwy0Wq19O7dW+5wBOEVnp6ezJ8/n+DgYNq1a8dXX31FkSJF8PPzy9DEWLGxsUZtZ2dnx4gRIwgODqZLly4oFIp0v/A1Gg1Lly41uvy3adWqFWDoj2Gq25a///77GxesS5mfo3z58mzcuJGmTZuapFxByCrEAmkvCQuDJ08gIgLi4yEiIoaPP86Bu3te1qz5G7Vagbs75MljeLxmjiBBkM3z58/5/fff+fXXX0lOTqZv376MHTuW/Pnzv/ZvJEmiRo0aTJgwgbZt22aovLNnzzJs2DDOnTuXuq8U7u7uhISEpLnt+Dbx8fD4MYSGQlQU6PUQGQk3bgQyeXJ9Wrb8hMmTF5AjB+TPD+/auBgXF0eBAgXSnc5drVaTnJyc+p60bt06ze0aQRCMsj5LJhmSBNeuwYULhn+vXIGgmxKPHytITPdHTQegIvB1mmdVKsiVW8LTU0G5slC2LJQvD9WqQTqjXwXBoqKjo1myZAk//vgjoaGhdOnShUmTJlG8ePFXtt2zZw8tWrRApVKxbt06OnXqlKGyJEli5cqVjB49msjIyNTbDwqFgoCAgHT3FxICp0///zN47brEg38UvH4JFy2QEzgOlE191t0dChWWKFtGQblyUKYM1KwJuXO/Oeaff/6ZMWPGpOk0q9Fo0Gq11KxZk++//54mTZpk4F0QBOElWSfJuH8ftm2Dg4fg6FEICwWNHRQulkwBTy0FPZPJkVdH9tx6cuTW4eymR2MPGjuJswf+4MMyFcnmURRJDzGRSiJeKAl7qiL8uZLH99Q8uqPm/i0N0ZEK7OyhejVo1AhatoQaNUD8CBLkkpiYyPr16/nuu++4e/cuHTt2xM/Pj9KlS6duU7duXU6fPk1ycjJKpZIlS5a80+3BqKgovvvuO3755RcUCgXJyck0btyY/fv3ExMDe/bAvn1w+IjErZsKFArIU1BHoeLJFCymJXcBHR659WTPrcPNQ4+Ds4QCcM5m6Gi6ZeESfHwHEB+jICpMSfhzFaFPlTx/pOLBbQ0Pb6t5+lCFJEFJL4lGDRV4e0OLFvDfJVASExMpXLgwz549AwzJRXJyMs2bN8fPz4/q1au/13suCAKQ2ZOMhw9hxQrYuEni4gUFzq4SZaolUbp6IqWrJVHES2vU7Q69TofSyPsizx6puH7Wjmtn7bhx1p4n/6jIl1+iQ3sFPXoYfmEJghy0Wi1r165l+vTp3Lhxg1atWjFhwgRUKtUrX6oKhYJFixbRr1+/dyrr1q1bjBgxgj179qBQKGjifY/AwA/QaqFkBS2lqho+g16Vk3B0Nv4SJEnSW29bxMUoCLpgx/Vzhs/grcsa7OygWTPw7QIdOsDSpfMYMmQISqWhW5qvry8TJ05Mk3gJgvDeMl+SIUmGX0pz5sLOHeDipqdakwRqNk2gbI0k1BrLHu79W2rO7HPg1F5H/r6pplx5iaFDDAmHWCVakINer2fr1q1MnTqVs2fPUrRoUR4+fJhmGGfKF/mvv/7Kp59+muEybt+GefNgwYI9xMSMJN8HnWk34AuqNUkgW3bTLgf/NlFhSs4ccODMfgf+OmGPWzYtWm0x4uKe0LdvH8aNG8eHH35o0ZgEIYvIXEnG/v0wbjycPwfFymrx7hJHg4/isXOwjkO8c03D/vVOBO5wxMlRwaefwqhR4OYmd2RCVrVs2TL69ev32plDFQoFP//8MyNHjjRqf3fvwrTpsGQJZM+pp16bOHy6RHL3+i5qNWttwsjfTcQLJcum/8n5Q+dIiBtDh44F+f47KFlS7sgEIVPKHEnG6dMwdBhcvAA1vBPoNDSGoqVeP7mO3KLClWxf6syeNc442iuYMgUGDAClGFAsWNiAAQNYsWLFGyejAvjuu++YOHHia19/8QLGjYNlyyB/0WQ6DYmhTot4FFZ4Tut1OkDFsZ2ObJrnypP7Kvr3hylTQMweLggmZdtJRni44cK2aBGUrZ5E73FRFPGy3uTiZdERSjbNd2HXSmcqV4L586FSJbmjErKKp0+fUqhQobcmGCn8/PyYPHlymuckyfD5GzsWVPZ6un8eRb1W1plcpEevg8AdjqyamQ2FTsn06dCvn+ioLQgmYrtJxrFj8HE3iEvQ02tsFPVax8sd0ju7f0vN4m/duHXZjik/wOefi4ucYH7jxo1jxowZRk0RDoZbJxMnTuTbb78FDK0XffrAnj+hZY9YfIdHZ6gTpzWJi1Gw/ldXdq12plVLWLpUtGoIggnYZpIxbRpMmgSV6icy9IcIi3ckMwdJD38sdmHdr654N4G1aw3j/wXBHGJjYylUqFCaGUFVKhVqtRq9Xv/G1o0xY8bw0UfT6dwZ9EodI36KoGSlJEuEbXY3ztvh/0V2NEoFmzYqxGgwQXg/tpVk6HQwdCgsWgx9xkbRsmdspvvFH/yXhp9GeJAnp4I/9ygoUEDuiITMSq/X8/TpU0JCQnj8+DEhISE8evSIp0+f8uDBAx49esTDhw8JCwt7ZW0PlXoEVepP49NpkalzWGQWMZFKZo9159oZe9avgzZt5I5IEGyW7SQZycnQuTPs+VNi1M8RVG30+vUGbN2LJyqmDPRAl6DmyGEQo+sEuT179oynT5+ydOkj/P2fUqzc3/SbUIPi5SvIHZpZ6HWw8Bs3Dmx2YukS6NlT7ogEwSbZRpIhSdC7j2FSrUmLwjJN0+ybxEQp+a6fB/p4NSeOK8iTR+6IhKzujz+gUydoPzCGj0dEyx2ORaya6cq2pS78sQVayz8CVxBszXqb6AM+cSKsWwtf/hqeJRIMAJdser6aH0aiTk+LlvCGhSIFwexOnYKuH4OPb1yWSTAAuo+OpnGHOLr4wr/rvwmCkAFW35Kxf79hOuDB30bSpFOc3OFY3NMHKsZ0zEWfXgpmz5Y7GiErioyEChUlcn6QxPi5YTYzPNVU9Dr4YWAOokI0XLqowNVV7ogEwWZYd0tGeDj06gW1mydkyQQDIE8hHQP9Ivn9d9i1S+5ohKzo0+EQHSsxbEpElkswAJQqGD49nLAIic9GyB2NINgWq75kfP89xCfpGegXaZHyzuzfQ0ev/KkPbWKiRcp9mzot46nTMp4RIyWMnDdJEEzi5ElYvQoGfhOJW47MNYokI9xz6vnEL5Lly+DsWbmjEQTbYbVJxt278Nvv0OXTaIsNkavu3ZxNQY+p3qSZRcrLiO6jo/nnvoIFC+SORMhKPv8cytVIolpj0Smopk8Cpapo+eILuSMRBNthtUnG7NmQI48O7y5Z8zbJy3IX0OHdJZafZkpYdy8aIbM4ccLQktFtVJTcoViNbiOjOHpUdAIVBGNZZZKRlAQrV0GjDnGoVHJHYz2ad4vj73sKDh+WOxIhK1iyBIqUTKZ4BXGPLkWpqkkULpbMkiVyRyIItsEqk4wDByAsFBq2s931SMyhwIfJlKigZc0auSMRMjudDjZsMCT6QloNO8Sxbh0YueSLIGRparkDSE9gIBTy1JEjr07WOMJfPGPlTz9w6dhhlEolJStVpd9X35K3cBHZYipXM5Gjh9VAJptPXbAqly9DVBRUqGP+zs9n9u9h+qf9Uv8/e3cga/1/5PLJQGIiIwBYevIqw5rWJi46/Vs3CoWC+YfOkSNvPrPHW75mEit+hOvXoWxZsxcnCDbNKlsyTpyUKFZB/pEdS6d8Teven7Do6AW+8F/A9XOnmfX5UFljKlkpieBbCsLCZA1DyOROngSXbBIFP0x++8bv6eUO1/O+HkPzbr1ZcOQ809bvQPmfe6arLwSzKehx6qPrZ18C0G3UOIskGAAflNTi5CJx4oRFihMEm2aVScbff0OBovK2YgA06dyNkhWrYO/oRLmadana0JvbVy4RFS7fN3z+ojokCf75R7YQhCzg/n3I/4FOlnkx2n0yjDLVa2Pv4EjxCpXZcO0B2bJ7vLLdid3bWD/7Jxq196XDwOEWi0+pgryFdNy/b7EiBcFmWWWSERaqwNVd/huexcpVTPN/jzyGX0rhz57KEI1Byvvy4oVsIQhZQGgoOLvLk+gXL18p3edXng3CwckZgOC/LvDruBGUrlqTwd/+aMnwAHDNric01OLFCoLNscokIz4e7BzkH6fp7JItzf+VSkM/CL2MPb7sHQ3vS2ysbCEIWUBcHNjZy/MZdHB0euPrL548YtrQvuTMm58xvy1GrdFYKLL/s3eQiImxeLGCYHOsMslwc4eYSNGxMT0p74vHq63HgmAy2bNDbJT1XR7iY2OYMqgXyclavpq/Ehc3d1niiI1SkiOHLEULgk2xvqsIkDOnRFS4mCAjPVFhhvclZ06ZAxEytZw5IdrKPoN6nY6fRw/h4d3bjJm9iPxFPkx9bcZnn3Bm/x6LxRIVJpIMQTCGVSYZZcsq+PuG5ZtAbcHd62rsHcDTU+5IhMysTBl4eE9FQpz1tCgunTqZC0cOMOS7HylTvbZsccTFKHh0XyWGrwqCEawyyahTG25d0lh8+uxbf52no1d+zhz4E4CuFYqy5pfpAHT0ys+Whb8D8EV7H6YM7mXZ4P5186IdVaqAnZ0sxQtZRO3aoEuGO1fNn+yn97nr6JU/zTZ3rl1m1yrDNJu/jR+VZiHDjl75ObV3p9njTBF82Q69DmrVsliRgmCzFJJkfSthXL4MFSrAD2tC8aqcJHc4VkOngyGN8/DpYCV+fnJHI2R2xUtIlKwVR7+vxNol/7XwWzf+vujEjetyRyIIVm+9VbZklC8PFSpKHNzkKHcoVuXCEQfCninp2VPuSISsoG8fBYHbndAmWc8tE7klJSg4vtOR/v3evq0gCFZ6uwTgkwEKju9yJPy51YZocTtXONO4seiPIVhG796G/gdHt4lkP8WhLY4kxCtEoi8IRrLab/D+/SFXLgXrZ7vKHYpVuHDEniun7MRtEsFiChSAgZ/A+tmuJMaL1oyEOAUb57jy6TDIk0fuaATBNlhtkuHgAD98Dwc2OXH7StYeaZKUoGDFjGy0bQd168odjZCVTJ4MibFKNs51kTsU2QX87kpyopKvvpI7EkGwHVabZAB07w6NG8Evn2cnLibr/pJaNi0bUc/V/DJL7kiErCZ3bvjpJ/hjkQtXTmXdIU3XztixfZkzs2aJOWoEISOsOslQKmHFCtDGqZg3yR1J/uVMLO7odkf2rndi0SIoUkTuaISsaNAgaNsOfhuXndAQ65qgyxKeP1bh/0V2OnWCfqLDpyBkiFUnGQD58kFAAJw96MDSqdne/geZyKVj9sz5yp0vvoBOneSORsjKFi+CXB4KfvjEg5hIq79smExUuJIfPslB/rwKFi6QOxpBsD02cbVo1AhWr4I9a5xZNdPV4pN0yeHyCXtmfJadrh/D9OlyRyNkddmzw769CvSJan74xIOocJu4dLyXyFAl3/V3QalT8uceBdmy1m8cQTAJm7lSdOoES5fC9mUu/D7eHV2y3BGZz9FtjkwZ5EGH9goWLwJF1u2OIliRAgXgwH7QRmuY1C0nTx9k3lsnIf+omfBxdh7crkaZ0r48fnxe7pAEwSbZTJIB0LMn7NwBZ/c74tcnJy+eZK6LnC4ZVvyYjV/HujNyJKxaCTKsYi0Ir1W8OJw4ATndVXzVNScXjtjLHZLJnTvkwHjfnOTPpeLnmeO4f/861apVo02bNgQGBsodniDYFJtKMgCaNoWTJ0GK1fBl+1yc2ucgd0gmEfKPmondcrJvvTNLl8KPP4oWDME65c0LgUfho1ZKpgz2YNm0bGgTbf9kTUpQsGRKNqYNzU6HdgqOHlXx6ae9uHLlCnv37gWgfv36VK1alRUrVqDT6WSOWBCsn1WuXWKMuDgYMQIWLYLqTRLp+1UkuQvY3odem6hg80IX/ljojJcXrF+nwMtL7qgEwTgrV8LQoeDqoaPfxEgq10+UO6R3cu6wPUt/cCM2QsXcuYbh8+k5f/48/v7+rFmzhqJFi/Lpp58yaNAgHBwyx48dQTCx9TabZKQ4eBCGDoP79yVa9ozlo36xuLpb/1hXnQ4CtzuycY4r0WEq/Pzgs8/E7RHB9jx6BKNGw4YAqNY4Ed9PoylaWit3WEa5c1XD+tmunD9iT9ePYeZPkD//2//u9u3bzJ49mwULFuDu7s6gQYMYOXIk7u7uZo9ZEGyI7ScZAElJMHs2TJsGcQkSzbrF0qJbHDnyWl/LRlKCgmM7HdmywIWnj1T07gXffWfchU0QrNn+/TB+PJw/b0g22vaPsdpVlK+ftWPrYhfOHbanWnX4cTo0bJjx/YSEhDBv3jx++eUXJEmiT58+jB07lvziAy0IkFmSjBSxsTBnDsz4CUJDoWrDRLw7x1KxbiIqtbyx/ROsZv8GJ45udSIhXkGP7jBxInz4obxxCYKprV79lMGDOxITM5siJcrh7RtH3VbxsrcwRkcoCdzhyL51TvxzW02NmjD5a2jR4v33HRUVxdKlS/nxxx8JDQ2lS5cuTJgwgZIlS77/zgXBdmWuJCNFUhJs2QLz5kkcOaLA1U2iaqMEajRNoGyNRByczH/Ieh3cva7hzH4Hzux35MEdFUU/lBg0UEHfvobpmgUhswkJCcHb2xutVsusWQfYvLkga9dBYiKUrZ5EjabxVGmQSM58lmllfPFExfnD9pze68jVs3bYaYLp2bM4AwdClSqmLy8+Pp6lS5cyc+ZM7t+/T9euXfnqq68oXbq06QsTBOuXOZOM/7p3DzZtgk2b4cxpUCjBs4yWUlWSKFY+icLFk8lXJBnVe46GDXuq4sFtNXeuaQg6b0fQeTtiYxQU/kCiU0cFHTpArVqGqdIFITN68OABjRs3Rq1Wc+DAgdRbBtHRsHOn4TO4axfExULeQjpKVUmiZOUkipTUUrBYMo7O73cpio9V8OC2mvs3NQRdsCPonB0hD1U4O0PLVlC61GGmTGnK7t27adKkiSkO+bWSk5MJCAhgypQp3Lhxgw4dOjBhwgQqVqxo1nIFwcpk/iTjv54+hSNH4OhROHRY4maQAp3O0NmyQNFkcuTT4ZZTR468epxd9Dg4S6hU4OCsJzlJQWKCAm2SgrhoBREvlIQ9VRHxXMXjv9VERxqG8OXLL1G/noL69aFBAyhTRuaDFgQLuH//Pk2aNMHe3p79+/eTL1++dLeLj4dTpwyfwyNHJM6cVRAXaxiunaegjpz5dHjk0eGeU082Dz3O2fQoFODsarjVEhutRJIgNkpJVJgy9XP44rGKkIeGXwrOzlC9ukSDBgoaNICaNQ2rOkuSRI8ePdizZw+nTp2iePHiZn9fJElix44dfPfdd5w9exZvb2++//57atSoYfayBcEKZK0k42UJCXDjBly9CjdvwsOH8OQJPHgoER0NMdEKtFqIjV2AWuONi/OH2DtIuLpCnjwKChU0zBlQvLghmShXDjw85D4qQbCse/fu0aRJE9zd3dm7dy85M7BMqV4Pf/9t+AxeuwYPHsDDR/D4sURoKISH30WbdJj4+D6ACjc3Q2ukm5tEzpyQL5/hc1iwIJQta/gcFi36+jlmEhISaNCgAVFRUZw8edKio0H279/PpEmTOHXqFHXq1OGbb74xe4uKIMgsaycZxpAkCaVSSUBAAJ07d5Y7HEGwKjdv3qRJkybkzZuXvXv34mHiLHv16tX07duXxMREFCaane7JkydUq1aNsmXLsnPnTlTve680g44dO8Y333zD/v37qVOnDmPHjqVNmzYWjUEQLGS96CEgCMI7uXHjBo0aNaJIkSIcPHjQ5AkGQFhYGB4eHiZLMADy5cvH1q1bCQwMZPz48Sbbr7Hq1q3Lvn37CAwMJHv27Hz00UdUrlyZDRs2IH7zCZmNSDIEQciwS5cuUb9+fYoVK8bu3bvJZqYlSsPCwsiRI4fJ91ulShXmz5/PjBkzWLx4scn3b4y6deuyfft2Lly4QLFixfD19aVixYpiynIhUxFJhiAIGXLhwgW8vb0pW7Ysu3btwtXV1WxlhYeHm6WFBKBHjx6MGTOGTz/9lFOnTpmlDGNUqlSJgIAALl26RIUKFejXrx8VKlQQyYaQKYgkQxAEo507dw4fHx+qV6/Orl27cHFxMWt5KbdLzGXq1Kn4+PjQvn17Hjx4YLZyjFG+fHlWrFjBpUuXKFeuHH379qVMmTKsWbMGvd76l0oQhPSIJEMQBKMcO3aMxo0bU6tWLTZv3oyjo6PZywwNDTVrkqFUKlm9ejU5c+akbdu2xMXFma0sY5UtW5a1a9dy/fp1qlevTq9evahQoQKbN28WfTYEmyOSDEEQ3urIkSO0aNGC5s2bs2XLFoutOmrulgwAV1dXtm/fzsOHD+nVq5fVfJGXLFmSFStWcPXqVSpVqkSXLl2oUKGC6CAq2BSRZAiC8EZ79uyhRYsWtG7dmjVr1qCx4FLBYWFhZM+e3ezlFClShE2bNrF9+3amTJli9vIywsvLixUrVvDXX3/h5eWFr68vtWvXZvv27XKHJghvJZIMQRBea8eOHbRr1w5fX19WrVqFWm3ZlQYt0ZKRol69evz8889MmjSJgIAAi5SZEWXKlEntIFqoUCE++ugj6taty6FDh+QOTRBeSyQZgiCka+fOnXTq1IlevXqxePFii09aJUkSERERFksyAIYNG8agQYPo378/ly9ftli5GVG+fHkCAgI4ceIE9vb2NG7cGB8fH86ePSt3aILwCpFkCILwir1799KpUye6devGvHnzUMqwsl9kZCTJyckWTTIAfv31V6pVq8ZHH33Es2fPLFp2RtSqVYsDBw4QGBhIUlIS1atXx8fHhwsXLsgdmiCkEkmGIAhp7Nu3j7Zt29K1a1cWLVokS4IBhlslgMWTDI1Gw8aNG9FoNHTo0IHExESLlp9RdevW5ciRI+zbt4/w8HCqVq1Kly5duHnzptyhCYJIMgRB+L/9+/fTtm1b2rZtK2uCAfIlGSllbtu2jatXrzJ48GCLl/8uvL29OXv2LFu3biU4OJjSpUvTpUsXbt++LXdoQhYmkgxBEAAIDAykffv2tGnThlWrVlm8D8bLUpIMc0wrboxSpUqxdu1aVq5cyezZs2WJIaMUCgVt2rTh/PnzLF++nIsXL1K6dGmGDBlCSEiI3OEJWZBIMgRB4NixY7Rs2ZIWLVqwevVqi48iSU9YWBhqtdps66IYo0WLFnz33XeMGjWK3bt3yxZHRimVSnr06MGNGzeYM2cOO3bsoFixYkyePJno6Gi5wxOyEJFkCEIWd/z48dSJttasWWMVCQb8f44MU67A+i7GjRuHr68v3bt3Jzg4WNZYMkqtVjNgwADu3LnDDz/8wK+//krRokWZPn261fc1ETIHkWQIQhZ24sQJWrRoQdOmTa0qwQDLzpHxJgqFgsWLF1O8eHHatGlDRESE3CFlmJ2dHSNGjODOnTsMGDAAPz+/1BlFxeyhgjmJJEMQsqiTJ0/SvHlzfHx8WLdunUVn8jSGtSQZAA4ODvzxxx/ExsbStWtXm10d1cPDg2nTpnHr1i2aNWtGv379qFGjhpjQSzAbkWQIQhZ0/vx5WrVqRb169Sw+VbixrCnJAMiXLx9bt24lMDCQcePGyR3OeylUqBDz58/nr7/+okiRIqkTel27dk3u0IRMRiQZgpDFXLhwAR8fH2rWrMnmzZuxt7eXO6R0WVuSAVC5cmWWLVvGzJkzWbRokdzhvLeUqcr//PNPQkJCqFy5Mp9//rlN3hISrJNIMgQhC7l48SI+Pj7UqFHDqhMMsM4kA6Bz586MGTOGYcOGcfToUbnDMYmmTZvy119/sXDhQlavXo2npyfTp08nKSlJ7tAEGyeSDEHIIi5duoSPjw9Vq1a16HLt78pSK7C+iylTptC8eXO6dOnCgwcP5A7HJJRKJb169eL27dsMHz4cPz8/ypcvz4YNG+QOTbBhIskQhCzg0qVLNGnShKpVq7J161arTzDAelsywPCFvHLlSnLmzEm7du2Ii4uTOySTcXFxwc/PjytXrqTOGtqiRQuuX78ud2iCDRJJhiBkctevX6dp06ZUrlzZJlowUoSHh1ttkgGQLVs2tm7dyv379xkwYECmGwparFgxNm/ezKFDhwgJCaFixYp8/vnnREVFyR2aYENEkmFGq1atQqFQpD5cXFxe2ebSpUu0atUKd3d3XF1d8fb25vjx469sN27cuDT7qlmzpiUOQbBxd+7cwcfHh2LFirFlyxYcHR2N/ls5z9/o6GiSkpIylGQYEy/Arl27KFGixBvnBDE2Xk9PT9avX8+GDRuYMWOG0bG+qzcdY3h4OPPmzaNx48Z4eHjg6OhI8eLF6d69O3/99dcr+zL2GBs2bMj58+dZtGgRK1eutPr5NeQ4D4TXE0mGBcydOxdJkoiJiUnz/OnTp6lduzaurq7cuHGDe/fu8eGHH9KwYUP27t2bZttp06YhSRKSJMm+poRgGx49eoSPjw+5c+dm586dr73Yvo0c5+/7rFvyunjv3LnDRx99xPjx43n69Okb95GReJs0acL06dMZP348u3btynC87yK9Y/zyyy8ZPnw4bdu25fr164SGhrJkyRIuXbpElSpV+OOPP9LsIyPHmNJf4+bNm3Tp0iV1fo0zZ86Y4/BMwtLngZA+kWTIRK/X079/f9zd3Vm6dCn58uUjZ86czJ07F09PTwYMGCCm/RXe2fPnz/Hx8cHOzo49e/aYvAOluc9fc6zAOmnSJGrXrs358+dxdXU12X4BRo8eTe/evenRo4esq57269ePESNGkDdvXpycnFLnQdHpdIwZM+a99589e3b8/f05e/YsdnZ21KpVi169evHixQsTRG8Z5jwPhFeJJEMmR48e5dq1a3Tq1ClNE7ZKpeLjjz/mwYMH7NixQ8YIBVsVGRlJ8+bNSUpK4uDBg+TJk8fkZZj7/DVHkrF48WLGjRtntqnT58yZQ7FixWjTpo0s/RYWLVrE/PnzX3m+QoUKODo6cufOHZPd4qhUqRKBgYEsXryYvXv3Urp0aRYvXmy1t1D+y9zngZCWSDJkcvDgQQCqVq36ymspzx04cMCiMQm2LzY2ltatW/Ps2TP27dtH/vz5zVKOuc/fsLAwlEolbm5u77yPl2WkP8q7cHBwYNOmTYSFhdG7d2+r+cKNjY0lPj6esmXLmnSxOYVCQZ8+fbh58ybdunVj8ODB1KtXjytXrpisDHMw93kgpCWSjAwKCgqiXbt2uLm54eTkRPXq1dmxYwfe3t6pnYMGDBhg1H4AChYs+MprBQoUAODWrVumDV7I1OLj42ndujU3b95k7969FC1aNN3tQkNDGT16NJ6entjb21OwYEG8vb1ZtmwZ8fHxRpVl7vM3LCwMd3d3VCqVSeK1lEKFCrF582Z27drFDz/8YPTfmfMYU+a5mDBhwnvt53Xc3Nz45ZdfOH/+PHq9nsqVKzNixAiTLilvquuuYHmivSgDbt++Ta1atXB2dmbjxo3UqlWL+/fvM3LkSC5fvoy9vT0JCQlG7Stl2l5nZ+dXXkvpoBceHm6y2IXMTavV0rlzZy5dusTBgwcpVapUutuFhIRQu3Zt4uPjWbhwIQ0aNCAuLo6FCxfSt29fIiIiGDly5FvLM/f5GxoaioeHh8nitaQ6deowc+ZMRowYQcWKFWnduvUbtzfnMT59+pRx48YxYMAAunTp8k77MFb58uU5fvw4K1eu5PPPP2fjxo1MnTqVXr16vdd+TXndFSxPtGRkwFdffUVERAT+/v74+Pjg4uJCmTJlWLNmDbGxsSYrJ6WZ1ZRNm0LmpdPp+Pjjjzl69Ch//vknlSpVeu2248eP5969e/j7+9O6dWtcXV3JkycPEydOpHnz5iaJxxTnb8ocGZaI1xw+/fRT+vfvT7du3d46iZW5jjE0NJTmzZvTsGFD5s2b9877yQiFQpE6CqV169b06dOHNm3a8Pfff7/zPi113RXMQyQZGbBnzx4AmjVrlub5XLly4eXllaF9ubu7A6T7IUl5LmUbQXgdSZL45JNP2LVrF9u3b6d69epv3H7Lli0AtGjR4pXXdu/ebfQvZnOfvymzfZoqXjn8/vvvVKxYkQ4dOhAZGfna7cxxjLGxsTRr1ozSpUuzevVqiw+/9PDwYP78+ezfv5/g4GDKli3LjBkzSE5OzvC+THndFSxPJBlG0mq1REdH4+DgkO58AxkdIpjy4Xj48OErrz169AiAEiVKvEOkQlYyZswYVq1axYYNG2jQoMEbt01MTCQyMhIHB4f3Hrpn7vM3pU+GqeKVg0ajISAggJiYGHx9fdHpdK9sY8o6SZGcnEznzp0pUKAAy5cvl3V+h8aNG/PXX38xduxYvv76a2rUqMHFixeN/vvExESTXncFyxNJhpE0Gg2urq4kJCS8MrkLwLNnzzK0v0aNGgFw/vz5V15Lea5JkybvEKmQVUyZMoWZM2eyYMECWrVq9dbt7e3tcXNzIyEh4b075Zn7/A0LCyNnzpwmi1cuefPmZePGjRw+fJhvv/32lddNWScpBg0aRGJiIgEBAWmGaRYrVoxTp06ZpIyMsLe3Z9KkSVy9ehU3NzeqV6/OiBEjjLrVYW9vb9LrrmB5IsnIgJTmzJTmuxQhISEZ7knfoEEDSpcuzcaNG9N0WtLpdKxbt45ChQoZ9cUhZE3z589nwoQJ/Pzzz/Tp08fov2vfvj1AujNTVqpUiVGjRhm1H3Ofvym3S0wVr5xq1qzJ/Pnz+e6779Jd0dSUx+jn58e1a9fYunUr9vb27x60GXh6enLgwAF+//13li1bRoUKFYwa5mzK665geSLJyIApU6bg4eHByJEj2bdvHzExMVy9epW+ffuSN2/eDO1LqVSyePFiwsLC6Nu3LyEhIYSGhjJs2DCCg4NZuHChzSxkJVjWli1bGDZsGN9++22G79dPnTqVokWLMmrUKHbu3El0dDQPHz5k6NChPHnyxOgvNHOfvynLvJsqXrn17t2bwYMH069fP65evZrmNVMd47Jly/jmm284ffo0rq6uadbcUCgU3LlzxxyHliEKhYKBAwcSFBRE+fLl8fHxoVevXqmTr6XHlNddwfJEkpEBnp6enDx5kmrVqtGpUyfy5MnDoEGDGD9+PMWKFcvw/mrWrMmJEyeIjIykZMmSFClShODgYA4fPvxKJydBAMMEVx9//DGDBw9m0qRJGf77vHnzcvbsWXx9fRk+fDg5cuSgevXqhIeHExgYSOHChY3elznP37CwMHLkyGHSeHfs2JH6hfvo0SN0Ol3q/xctWvRe8RrD39+fKlWq0KZNmzTTcJvqGDdu3Giu0E0uX758bN68mfXr1/Pnn39StmxZNm/enO62pr7uyn0eZDVinowMKlGiRGpvcFOoVKmSxRZVEmzb6dOnadeuHZ07d+bXX3995/3kyJGDWbNmMWvWrPeOyRznb1xcHAkJCalTipsq3tatW8s6C6dGo2H9+vVUq1aNbt26sXv37tROmaY4RltchqBz5854e3szbtw4OnbsSOvWrZk/f/4rM9Wa8ror93mQ1YiWDEGwAVevXqVly5Y0atSIpUuXolRm3o+uOdYtsRZ58uRh69atHD9+3GwzcNqa7NmzM3/+fHbv3s2VK1coU6YMCxYsEIlAJpF5r1RWZMiQISgUindeahtg3LhxqU166Q2FEzKvu3fv0rRpU8qXL//KiAFLsPT5+75JhrV/3ipVqsT8+fP58ccfWbdu3Tvtw9qP8V00b96c69evM2jQIIYOHUqjRo0IDg5+5/1lxvfIJknCG+n1egmQAgIC0n197dq1EpDm0b9/fwtHKWRWISEhkqenp1S5cmUpMjJS7nAs4tChQxIgPXv2TO5QzGrkyJGSo6OjdO7cOblDsTonTpyQypQpIzk5OUnTpk2TkpOT07wurrs2Y51CkkSb1JtIkoRSqSQgIIDOnTvLHY6QhURHR9OwYUOio6M5duwYuXPnljski9i0aROdO3cmKSkpUy/HrdPpaNWqFUFBQZw9e5ZcuXLJHZJV0Wq1/Pzzz0yePJlKlSqxaNEiypQpI3dYQsasF7dLBMEKpSx49uDBA3bu3JllEgww3C7Jli1bpk4wAFQqFWvWrEGlUtG1a9d3mnI7M9NoNIwdO5Zz584BhttM48aNQ6vVyhyZkBEiyRAEKyNJEv379+fkyZP8+eefFC9eXO6QLCplIq6swMPDg82bN3P69GnGjBkjdzhWqWzZshw7dowpU6bg7+9PvXr1CAoKkjsswUgiyRAEK/P555+zfv16Nm7c+MYVVTOrlBVYs4oKFSqwYsUKfvnlF5YsWSJ3OFZJpVLxxRdfcOHCBfR6PZUqVWL69OmiM6YNEEmGIFiRqVOn4u/vz6pVq/Dx8ZE7HFlkpZaMFB06dODLL79k2LBhnD17Vu5wrFapUqU4ceIEfn5+TJ48mXr16ompxa2cSDIEwUqsWrUqdT2SrNzJOCsmGWBIMBs3bky7du14/Pix3OFYLbVandpXIykpiYoVKzJ9+nT0er3coQnpEEmGIFiBnTt30rdvXyZMmMCIESPkDkdWWTXJUCqVrFq1Cicnp9TRNcLrlS1bllOnTjF58mS+/vpr6tevz+3bt+UOS3iJSDIEQWZnzpzB19eXjz/+ON3lwLOarJpkgGH2y+3bt3P16lVGjx4tdzhWL6VV4+zZs8TGxlKhQgX8/f3FbKFWRCQZgiCj4OBg2rRpQ6NGjViyZAkKhULukGSXlZMMAC8vL5YvX86cOXNYuHCh3OHYhPLly3PmzBm+/PJLPv/8c5o3b86DBw/kDktAJBmCIJvHjx/j4+ND0aJFWbduXaafF8JYWT3JAGjXrh0TJ05k2LBhBAYGyh2OTdBo1b+yQgAAIzNJREFUNPj5+XHs2DH++ecfypUrx4IFC+QOK8sTSYYgyCAyMpKWLVvi7OzMrl27cHZ2ljskWZw+fZqgoCCePXtGcnIyiYmJxMbGZvkkA8DPz4/mzZvTpUsXHj16JHc4NqNmzZpcuHCBwYMHM2TIEFq2bCnePxmJacVf4u3tzZkzZ9Lc00tISMDOzi7NypcajYbLly9TsGBBOcIUbFh8fDzNmjXj7t27HD9+nA8++EDukGRTo0YNzpw5k/p/Jycn9Ho9BQoUoHDhwuTOnRsPDw88PDwYP358lkvGoqOjqVmzJtmyZePw4cPY29vLHZJNOXLkCH379iUqKoo5c+bQpUsXuUPKasS04i9r0aIF0dHRxMTEpD6Sk5OJi4tL/X9sbCyenp4iwRAyTKfT0aNHDy5fvsyuXbuydIIB0KxZszS3ieLi4khISODOnTscOnSIgIAA5s2bx/79+7NcggHg6urKli1bCAoKYuDAgXKHY3MaNGjA5cuX6dSpE127dqVnz55ERkbKHVaWIpKMl3z88cdpWizSo1Kp6N27t4UiEmxNSEjIa18bOXIku3fvZseOHZQvX96CUVknb2/vN67ZkdKiOGrUKEuFZHVKlCjBunXrWL16NXPmzHnl9WPHjjF9+nQZIrMNLi4uzJs3jz179nDw4EHKly/P4cOH5Q4r65BtAVgrVrduXUmpVL6ylHDKQ6lUSiEhIXKHKVipevXqSV988YWk1+vTPD9p0iRJpVJJmzZtkiky66PVaiUnJ6fXftYAKVeuXFJSUpLcocru22+/lTQajXT48OHU53777TdJpVJJuXLlknQ6nYzR2YZnz55J7dq1kxQKhfTZZ59JCQkJcoeU2a0TSUY65s+fL6lUqnQveCqVSmrSpIncIQpW6tq1axIgKRQKqXPnzqkXsXnz5kkKhUJatGiRzBFan9atW7/286bRaKQffvhB7hCtgl6vl7p06SLlyZNHCg4Olnr37i0pFIrU9+rAgQNyh2gzli9fLrm4uEhly5aVLl26JHc4mdk6cbskHZ07d37tfAWSJNGzZ08LRyTYinnz5qHRaJAkiS1btuDt7c3atWsZNmwY33//Pf3795c7RKvTrFmzN74+YMAAC0Vi3RQKBYsXL8bd3Z169eqxevXq1NtJGo2GNWvWyByh7ejVqxdXrlzB3d2dmjVrimnJzUiMLnmNli1bsnfv3ldW+dNoNDx//hw3NzeZIhOsVVxcHHnz5iU6Ojr1OY1Gg0qlwtfXl2XLlskXnBW7efMmXl5erzyv0Wjo2bMnixcvliEq63Ts2DHatm1LdHQ0Wq02zWuurq48f/5cjEDJgOTkZGbOnJk6LfnSpUvf2KE/KCgo3XNVeC0xuuR1evTo8Upmq1arad26tUgwhHStWbOG2NjYNM9ptVp0Oh07d+7k0qVL8gRm5UqWLEmBAgVeeV6r1fLZZ5/JEJF1WrBgAQ0bNiQyMvKVBAMgJiaGP//8U4bIbFfKtOT/ncBr9erV6W578uRJKleuLD7HGSSSjNdo167dK78IUoYfCkJ6Zs+ene7zWq2WiIgIateuLb4EXqNly5bY2dml/l+lUlGnTh0qVKggY1TWIT4+np49ezJo0CB0Ot0rraspVCrVa78ghTerVq0aly5dolevXvTs2ZMuXboQHh6e+npkZCRdunQhPj4eX19fEhISZIzWtogk4zWcnJxo27YtGo0m9TlHR0datGghY1SCtTp79iyXL19+7X3dlNksW7duLe6dp8PHxyfNr3O9Xi8WCPtXUlISLi4uKBSKN049n5yczNatW4mJibFgdJmHo6Mj/v7+7N69m2PHjlGpUiWOHDkCwJAhQ3j69CkAd+/eZezYsXKGalNEkvEG3bt3T73waTQaOnXqhKOjo8xRCdZo7ty5aRLS9CgUCnLlymWhiGyLt7d3ms7WefPm5aOPPpIxIuvh5ubG3LlzOXfuHKVKlUKlUr12W61Wy9atWy0YXebTrFkzLl26RPny5WnSpAm9evVi7dq1qd8FycnJzJ49m927d8scqW0QScYbNG/enGzZsgGGD2/37t1ljkiwRhEREaxZsybd++RgSFAdHByYOHEid+7coVu3bhaO0Pplz5499daIWq1m1KhRYsG4l1SuXJkLFy4wc+ZMHBwc0k1qFQoFK1eulCG6zCV37txs27aNb7/9lg0bNrwy2lChUNC7d29CQ0NlitB2iCTjDTQaDV27dgUMF8HGjRvLHJFgjZYvX57urJUajQaFQoGvry/37t3Dz89PtIS9QatWrQBD3wIx1Dd9arWaESNGcO3aNRo0aACQ5gtQp9Oxf/9+Xrx4IVeImUZycjKbN28mOTmZlwdh6vV6IiIixHlqBPFT4T+SkpJ49uwZz549IyoqiuTk5NS1JWrUqJE6Fa2bmxvu7u7kz58/S66nYGvCwsIICQkhMjKS2NhYdDodUVFRAGTLlg2VSoWzszNubm7ky5eP7NmzZ2j/v//+e5q+GCqVCp1OR506dfD39xfTh7/kdfWR8su8SZMm3Lx5853rIyv48MMP2bdvHxs2bGDw4MGvDGndtGkTgwYNMmpf5v582KpJkyZx6dKl13a0Tbk1tWrVKpMPCIiJieHx48dERkamrrUSGRmJXq/HwcEBR0dH1Go1bm5u5MqVi9y5c6fpOG1Nstw8GXq9nmvXrnH58mVu3LjBzaAgbl29yqMnTwj994OVEU729uTPnRvPkiUpVbYsXl5elCpViipVqogExIJiY2M5d+4c169f5/r169y4coXbt24R8uIFia+5jfE6DnZ25M2Rg2IlS1KqXDlKly5NmTJlqFKlCk5OTmm2PXToUGoLl0KhQKFQ4OnpyaxZs1J/mWdFctVHVhQaGsrnn3/OihUrUhPcWrVqcfz48dRtRH1kzKFDh2jSpMkrLRgvUygUODk5ce3atQwvdphSJzdu3CAoKIigq1e5c+sWj589Iy4xMcMx53RzI3/evJQsV46S/34PlS9fntKlS791PS4zWp/pkwydTsepU6fYt28fJwMDOXXqFFFxcdgplRTTaPDSavHS6ykI5AdyAbmB7BjuJTkCC4ChQMoUS1FAGPAEeAY8AoKBG3Z23NTriUxORq1SUbFMGWo1aEDDhg3x8fHB1dXVsgeficXFxXHgwAEOHDjAicOHuXj1Ksk6HdnVakoplZROSqI4kO8/D3fACUO9psx0EgnogTggHEOdhgCPMdTpdTs7buj1RCQno1GpqFSuHLUbNsTb25vGjRvTq1cvNm3ahEqlInv27EybNo0+ffrI+aGWhanqYzowkHevj6x8O+rw4cP079+fu3fvps4O+tdff8n6+bDF+pAkibZt27Jjx47Uz/HrWjPAcFu0evXqHD169I2f+6ioKPbu3cuRI0c4cfgwl2/cIFmnw02txkuppFRSEsWAghi+h/Jj+B7K9u/fZwNUQDyQAOiACAzfQc8w1MlD4KZSyQ2NhttaLVq9HjdnZ2rWrEmtevVo2rQp1atXf2PnYRNbnynXLomPj5cCAgKk/7V35lFWlGcefmq7vTc00M3SokFaAYl7i80O4hICMRjiGpMx476Nk5zJMSZxNDlJMCoq4hLEYSSeiZphNHoGNWKURQUREQVtEBAdaBq6m72hl9v3/uaP6lYDF+jl3ltfQz3nvKcOt5rvq/s+t6re+92vqq64/HJ1z88XoH6RiK60LD0K+gAUBamVEW/D3wr0OegZ0L+AzopE5FiWIq6rcaNH64EHHtDmzZuDTlGnZNeuXZo5c6Ymjh+vrEhEFuj0SES3Nud7Yxs9tSW+AP0ZdAvotOa+syIRWZYlz/N0xx13qLa2NugUpZVU+GjtvpbIR3ZGhi6cMEGzZs3S7t27g05P2tm1a5cef/xxnVhS4j8/J+D9o7P7qKqq0uzZs/W9733vy4f4eZ6X8Bk7tm3rD3/4wwFtVFRUaOrUqTpn1Ch5jiPHsjQkEtFtoGeb85YqJ42g5aBHQFdalo6LRASoR5cu+sEVV2jOnDnpeEDckfWAtCVLluj6665T19xcOZalca6r+0GrUyiyNVED+i/Q5Zalrq4rx7b17Qsu0LPPPquGhoag02Y88+fP1w+vvFLZGRnKdBx913E0E1QZoNPNoO+BjrUsZTiOcjIzddU//ZMWLVoUdLpSjqk+ngBd6DjKDH3od6CbQx9JIxqNatGiRbr99tvVv39/tRQcX39at+u6+uCDD1RfX69nnnlG3zr3XDm2rQLX1Q8sS38GbQvQiUCfgO4DjXFdOZalgrw83XjjjVq6dGmqUtf5i4xYLKaXXnpJw4cMEaBBnqe7SG2F2JGoB70Euth15dm2enbvrrvuukvbtm0LOpVG0eK17MwzBegk19U9oGoDHLbElublTtAM0PDmbwqnn3yyZs+eraampqDTmDQ6g4+WCH340dYR2NBH6/n444917733avjw4XIc58un4RYWFqq4qEiOZelcx9Fs0F4DHCSKzaCH8EeeAJ156qmp8NK5i4w5c+boxH79ZFuWvus4WmSAuLbERtC/gfJdV/nZ2br77ruPuiH3RLz44osacPzxsi1Lkx1HSw1w1dpYArrIcWRblk464QTNnTs36HR2mNCHWYQ+zGLjxo266KKL5DqOLFApaJMBuW5LLAB9p9nLoJISvfDCC8lKT+csMpYuXaoRZWWyQFfYduA/h3Q0doF+D+riuupTWKgnn3xS8Xg86DSnnY8//ljnnXOOLNCljqM1Brhpb5SDvt88lHrBuHEqLy8POr1tJvRhFqEPs4jFYpo5c6Z6de+uLq6rKaDtoHfwR6yDznF74hPQZbYtCzRq6FAtW7aso2nqXEVGXV2dfvazn8mxbY103U5VwbcmqkA3WZZc29aYESP02WefBZ3ytBCLxXTvvfcqw/NU6nl6ywAXyYoFoDM8T5mep6lTpyoWiwWd7sMS+jCL0Id5rFu3TqOGDZNn27rFsoz82bAjsQQ0vHn+4M9//vOOTBDtPEXGihUrNKikRPmuqycw5/fGVMQy0DddV7mZmXryySeDTn1K2bRpk0aUlSnDcXQPqMmA/Cc7mkC/A0VsW2OGD1dlZWXQaT8ooQ+zCH2Yx4wZM5STmalTPE/LDchfqiIOehyU57oafOKJWrlyZXvS1TmKjDlz5ignM1OjXdfYCZ3JjnrQ7fiXod18002KRqNBa0g6y5YtU3FRkQZ5nj40IOepjuWgEz1PfXv10ooVK4JO/wGEPswi9GEWjY2NuuH662WBfgFqMCBn6YgNoBGuq9ysrPbM1TC/yJgyZYosy9LNlqVGAxKe7pgDynEcjRs9+oiaFPryyy8rJzNT57uudhqQ53TFdtA5zTvsa6+9FrSGLwl9hD5MCFN97N69W2NGjFCu4+h5A/KU7mgEXW9ZsixL9913X1tSZ3aR8dvf/lYWaLoBSQ4yPgAVua5GDx9+RBQa8+bNU6bn6SrbbtNN0Y6UaARdadvKzsjQm2++GbSO0Efow6gwzcfu3bs1oqxMvTxPHxmQnyDjIfzR9Xvuuae16TO3yJg6daos0B8NSKwJsRJU5Hk6Z9QoNTY2Bq2n3SxZskTZGRm60rYVMyCvQUUT6FLbVm5WVjJmcIc+Qh9HVJjio6GhQaOGDVMvz9MnBuTFhHgEv9B4+OGHW5NCM4uMefPmybFt3W9AQk2KD0G5jqN/ufXWoBW1i+rqavXt3VvjXfeo/Ia2fzSCznMcfaO4OJCbsYU+Qh8mR9A+JOnGG25QnutqlQH5MCnuAbm2rTfeeONwKTSvyNi0aZN6dO2qK2w78ESaGM/hV5HPPPNM0KrazPjzz9dxnqcaA/JoSlSB+rquvjN+fOjDgAh9mBVB+nj66adlgf7HgDyYFnHQJbatooKCwz2Ly7wi4+LJk3VCJBLIrVif5h8fepOT4G8+AH0b1AWUCxoHCa9bv32/ts5O4nbebFkqKijQ9u3bg9bVav7yl7/IAr0dOj0gFuAXjkm8y16n9iHQXNAJIOcQbYU+UutjO/4ljGNBBaBMUAnoCtCKI9xHTU2NunfpotvS7KMz7Sd7QMd7nq647LJDpdKsImPevHkC9ErAYh8/yPoloCzQpfj3fa8GXQtyQX87RLtOEsUK/1kAPT1PN990U9DKWsW+fft0XJ8+uiqA0anO4vRK29bxffuqrq7uqPaxDvQd0CmgfA598Ax9pNbH1fj7wUP4D7/bC1oIOqk53y8coT4k6bprr1Ufz9PuNPvobPvJSyDLsg41QdesImNkWZkmOE4gUg8nNgYaDOoN2ve115tAA0B9OfitZJMtVqCZoIjrGn/jGkl64oknlOU4gTyls7M43QTKsG3NmjXrqPUh0OWgKaAoqJjgDp6hD7/IuC7B6yua/88JR6iPjRs3ynUcPRWAj862nwh0vuPonFGjDpbOZ20MYdWqVSxasoSfxmJBb0pCFgIfA98Hsr72ugNcDmwE/jeN2/NDoKtlMWPGjDT22j5mPPIIl0j0CnpD9sMkp8XAZOCPDz+c8r5M9QHwH8DPATfg7Qh9wJNAoqPLqfj7y3r8M2E6SKuPGTPoYdtcnvKe2o8p+wnAT2Ix3li4kJUrVyZcb0yRMWvWLAZEIowNekMOwhvNy9IE61pe+3uatgUgA/hxNMrsmTPT2GvbWbVqFe9/9BHXxuNBb8oBmOb0unicpStWsHr16pT1YbIP+MdiL2hCH4nZC9QB3wSsNPabDh8As598kqujUSIp7aVjmLSfXAD0j0R4+umnE643psh4829/Y0JjY9I/tKuBSUAXIBsYgv/t9Fz8HcQCrmllOwDHJFhX3Lz8tCMb2g6+DWyoqODzzz9Pc8+tZ/78+XRxXcqS3O424KdAf/yC6xh8p0/hHwBbg2lOhwN5rsuCBQtS1ofJPkwj9JGY/25e/rKD7bSVdPhYu3YtG7dsYUIK2k7Wucg0LODbjY3Mf+21hOuNKDJ27tzJR+XljE5yu+uAocAyYA5QBfwnMA34CH/nE/6w4GG3sXmZk2BdbvNyRwe2tT2cDWQ5Tkp3uo6yaMECRkg4SWxzC3AW8Ay+yxrgfWAM8GMSD/EmYmfz0hSnLv6BdNHChSnrw2QfphH6OJCt+MP01wCXdGgr2046fCxcuJAcx0k4utkRknkuMpExwPKVK9mzZ88B64woMj7//HPiEiclud1f4J9IpgHn4Z84BgN/xh/ySxYtv0umc+gQ/A9mf9dlw4YNae659Wz49FMGJXmezR3ABnyvE4E8oCfwK+BbSeojKKcDm5rY8Gnqxk86q4+gCH18xbbm/z8G+GPHNrHdpNrHZ599xgmui5fkdtN1LgqKQUAsHueLL744YJ0J80aorq4GoEeS2321eXnBfq8XAgPxJ/21lq7Ny0QfiL37/U06KYzHqaqqCqDn1lFTU5N0ry80L8cnWPdKG9rp2rw0yWkhUJ1Cnyb7MJHQh89e/OPoScCfIKkjL20h1T6qq6spTMHFB8k8F5lIUfMy0bnIiJGMvXv9Q3p2EttsAPYAmXw19P11CtrY3sDm5aYE6yqalye2sc1kkBuLUVtbG0DPrWNfXV3CnyPaSwOwC99rXgfbMtFpDlC7N3XfbUz2YSKhD2gCLsafpzSb4AoMSIOPffvITvIk3GSfi0yk5TO8N4EbI4qM7t27A7A9iW1m4O9k9UCiU3Bba+GWq17eT7Cu5bVxbWwzGdS4LoWFhQH03Dq6FRSwLYntZeBPnKrH33E7golOtwHdu3VLWfsm+zCR0Adcj3+i/Av/OPRdAixJUh+tJeU+unVju5vcAf5kn4tMpKZ52aPHgeNyRhQZLSfJrUlut2W48NX9Xt9C268aGI0/VDgH/8PSQgx4FugLKZmRfDi2WlZCsabQo7Aw6TvRRc3LlxOsOx34SSvbMdHpVqCwZ8+UtW+yDxM52n3cjT+U/yL+yTJoUu2jqKiIrVbyZ2Il81xkIi2f4URfeI0oMkpKSsjLymJpktv9PdAN+FdgHn4VuQp/hnVbb3xj498AZXvz/9+CX1XfDKwFZuIPh6WTamBDQwOnnnpqmntuPaeUlrIsktwrzqcA/fAPlnPxv7FtAm4CKmn9QdREp+9FIpxamuy57V9hsg8TOZp9PAX8GngX/5u4tV+sT+ZGt5JU+zj55JNZ19iY1NElSO65yETeBfKzs+nXr98B64woMlzXZejQoSxIcgXZH1iMfznX9/FnWF+PP/u6pB3tlQHv4P/mOQD4Bv7JaD4HTuhJBwsAx3EYMWJEAL23jlGjRrE8Gk04TNheegHvAZcCtwLd8a853wEsAo5tQ1smOd0FfBSNMnLkyJT1YbqP/+Wrk1gF/qhSy7/TfXnf0e5jThK3KRmkywf4eUomyT4XmbSfACywbUaMHInjHDhjx4irSwC+NXEiv164kNqmpoSTY9rLiXw12zoZnE7iYcggeNZxGHrWWeTn5we9KQdl7NixWLbNC7EYP0xiu92BB5ujo5ji9Hn8gnvMmDEp68N0HxNJ362qD8fR7iOdj0loDenwUVBQwNlnnslzy5czKckTQJN5LjJpP9kNvGzb/G5C4h+XjRjJALjqqquIOg6Jb0wasj8VwEsSN912W9CbckgKCwuZNGkSj3vJvvL8yOMxz+Piiy+mWwontoU+Wk/owyzS4QPghltv5X/46gqzkEPzFCDP40c/+lHiP0j5I+3awDVXX63+nqe6NDzlbhwo4yBPvmuJnA60f/t+bSX7yXc3W5b6FBaqoaEhaG2H5c033xSgBWnwun90FqevN7f59ttvhz5CH6GPAH3U19erZ7duus2y0uKgM5+L9oG+4Xm68YYbDpZOsx71XllZqfzsbP0mhUKf2S/h4D/SOB0fpmTFRyDXtjV79uyglbWa8eefr8Gep6gB+TMtmkAne56+O3Fi6MOACH2YFUH4mDlzplzb1ooUvq8j4Vx0JygvK0sVFRUHS6VZRYYk3XvvvcpyHK00IIEmRj2o1HU1bMgQxePxoHW1mtWrVyviurrPgByaFlNAGZ6ntWvXhj4MiNCHWRGEj1gsprPPOENlrqsGA3JgYqwAZTqOHnjggUOl0rwio7GxUaOGDVOJ52mHAYk0La6xLHXJydGaNWuCVtVm7rnnHnm2rUUG5NGUmI8/KjV16tTQhwER+jArgvRRXl6uvOxs3Zimn006U2wD9fM8jR05UtFo9FBpNK/IkKQtW7aouKhI5zlOWuZndJaYCrItSy+++GLQitpFPB7XhRMmqI/n6TMD8hl0rAX18jxNnjQpkFGp0Efow+QI2ockPf/887IsSw8bkA9TYh9orOvq2N69VVVVdbgUmllkSNL777+vgrw8fct1w0IDNA1kweGGpoxn586dOuOUU9TP8/R/BuQ1qPgcdKzn6azTTtOuXbtCH6GP0IeBPiT/J3wL9KgBeQk69oHOcxx1z8/XihUrWpM+c4sMSVq2bJkK8vI0znG0zYAEBxFx0G/wC4z77rsvaCVJoaamRicPHKj+nqfVBuQ43fEx/lDjaYMHa/v27UHrCH2EPowK03xI0pQpU2SBfo9/TA46R0FEDWiM66pbfr6WL1/e2tSZXWRI0gcffKC+vXurJBLRxwYkOp2xF3SxbctzHD322GNBq0gqW7duVVlpqQpcV/MMyHW64hVQF9fV8CFDVF1dHbSGLwl9hD5MCFN9SNL06dPlOo4ucxztMyBX6YyVoOM9T8f16aMPP/ywLWkzv8iQ/Dkaw88+W7mOo8c5OirJpaCTPE89unbVG2+8EbSClFBXV6fLL71Urm3rLlCjAXlPVTSCfglyLEs//MEPVF9fH3T6DyD0YRahD/N4/fXX1S0/X4M9T+8ZkLdURxz0CCjHcTRq2LDWzMHYn85RZEhSQ0OD7rjjDrmOo3M9T+sNEJCK2Af6Bci1LJ07erS++OKLoFOfUuLxuKZNm6asSESlnqdVBjhIdnwEOsN1lZ2RYfyIVOjDLEIf5rFhwwaNHTlSrm3rl3DEjmqsA53junJtW7/61a/U2NjYnnR1niKjhXfffVcnnXCCMmxb/wZHzGWuMdBToL6ep9ysLD322GOd6j4YHaW8vFxDTj9drm3rFstSjQFOOhrVoBstS65tq6y0tFNddhz6MIvQh1nE43E98sgjys3K0rGep6fxj+FB5zQZsQ30E1DEtvXNAQP03nvvdSRVna/IkPx7aUyfPl09unZVd9fVb6DT7nRR0J9Bp3ieHNvWtddco8rKyqBTHAixWEwzZ85Uz27dVNDstTNO+K0B3QXq6rrq3aOHZs2apVgsFnR620zowyxCH+axefNmXXP11XJsW6d7np7Dv0Np0DluT1Q1eylwXRUVFOjRRx893D0wWkPnLDJa2Llzp+688051y89XtuPoZsvqNHcKrQY9gH/fd8e2dcnkyVq1alXQKTWCXbt26c4771RBXp5yXVc/BX1qgLPDxRrQv+L/ftk9L09333239uzZE3Q6O0zowyxCH+axcuVKXTx5shzbVj/P00N0ni++H+KPKGU1X5p61113JfOy4c5dZLRQW1uradOm6fi+fQXoDM/TNFClAQK/HvtAz4MmOY4itq3crCzdcsstWr9+fdApNJLdu3fr/vvv1zE9e8oCjXRdzcKsn8h2gP4DNMLzZIGO7d1bDz744BFz8Pw6oQ+zCH2Yx7p163TzTTcpJzNTEdvWRY6jv4Jx93qqAD0IOs3zBKjkuOM0ffp07d27N9kpOTKKjBbi8bjmz5+vH191lfKysmSBSj1Pd4PehUAePrQe9EfQRMdRtuPItiydO2aM/vSnP6m2tjbolHUKmpqaNHfuXF08ebIyPE+ebWuc62oa/rejdDtdDXoIf1KUZ9vK9DxddsklevXVVzvtsG9bCH2YRejDPPbs2aOnnnpK54waJduylO04+o7jaAYEcjfXRtBi0L+DzoxEZIHys7N19T//sxYuXJjK+X/PWpLUlmfHdxbq6ur4+9//zty5c5n7wgts3LqVbMfhTNtmaDTKmcCA5shMQn9x4HNgNfARsMS2WeI4bI1GycnM5LzzzmPChRcyYcIEevfunYQej0527NjBK6+8wkt//Suvvvwyu/bupcjzGBqLMTwe5xRgIHBcEvoS8H/4TlcA79g2ix2H6miUrrm5jJ8wgQsnTWL8+PF06dIlCT12PkIfZhH6MI/Nmzf756GXXuL1119nb309vTyPsliMsv2c2Enorw5Y0xzvA4s9j/fjcepiMY7t1YsJF13ExIkTGTt2LFlZWUno8ZA8d8QWGftTXl7O4sWLeeedd1iycCFrPvuMplgM27L4RiTCMRJ9GhspAoqAPPziIwLkAI3AXvxiYhewHdgCVLkumxyHddEo9fE4AMWFhQwZNozhI0dSVlZGaWkpGRkZAbzrI5toNMqyZctYvHgxby9axJK33mJzTQ0AuY7DCa5Ln6YmesZiFOM7bTnUFTQvdzQvdwG7gQqgynGocF3WNTVRG4sBvtOyESMYPnIkQ4cOpbS0FNd10/VWOwWhD7MIfZhHfX39l07eeestlr7zzpdOshyHEteluKmJoliMXkA3fCc2kAt4+OehRqAe2ANUAVuBykiEjcAX0ShxCddxGNi/P0NHj2bYsGGUlZUxcODAdL/lo6fI2J/GxkbWrl3L6tWrWbNmDZWVlVRs2kRVRQVVW7eyd98+6urraYhG2dfQgOc45GZl4TgO+bm5FBQU0OuYYyjq3Zvi4mL69+/PoEGDGDBgAF27dg367R217Nixg/Lycj755BPWrVtHZWUlWysq2LxxI7W1tezcvdv/u9paAApycwHomp9Pbm4uxcceS8/iYnr37k3//v0ZPHgwgwYNCp22k9CHWYQ+zGPnzp2sWbOG8vJy1q9fT0VFBVWVlWzZtIkdO3awu7aWWCxGbV0d0ViMnMxMIq5LVmYmuTk5FBYV0fOYY+hTXEyfPn0YMGAAAwcOpKSkhEgkEvTbO3qLjJCQkJCQkJCU8lwyfgIKCQkJCQkJCTmAsMgICQkJCQkJSQlhkRESEhISEhKSElzgv4PeiJCQkJCQkJAjjiX/D4GVi0whlJmeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=537x755 at 0x7F1F5E2E34F0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import CHGate, U2Gate, CXGate\n",
    "mini_dag = DAGCircuit()\n",
    "p = QuantumRegister(2, \"p\")\n",
    "mini_dag.add_qreg(p)\n",
    "mini_dag.apply_operation_back(CHGate(), qargs=[p[1], p[0]])\n",
    "mini_dag.apply_operation_back(U2Gate(0.1, 0.2), qargs=[p[1]])\n",
    "\n",
    "# substitute the cx node with the above mini-dag\n",
    "cx_node = dag.op_nodes(op=CXGate).pop()\n",
    "dag.substitute_node_with_dag(node=cx_node, input_dag=mini_dag, wires=[p[0], p[1]])\n",
    "dag_drawer(dag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, after all transformations are complete, we can convert back to a regular QuantumCircuit object.\n",
    "This is what the transpiler does! It takes a circuit, operates on it in DAG form, and outputs a transformed circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.154317Z",
     "start_time": "2019-12-10T21:48:03.916725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.977x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.converters import dag_to_circuit\n",
    "circuit = dag_to_circuit(dag)\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing a BasicMapper Pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we are familiar with the DAG, let's use it to write a transpiler pass. Here we will implement a basic pass for mapping an arbitrary circuit to a device with limited qubit connectivity. We call this the BasicMapper. This pass is included in Qiskit Terra as well.\n",
    "\n",
    "The first thing to do when writing a transpiler pass is to decide whether the pass class derives from a ``TransformationPass`` or ``AnalysisPass``. Transformation passes modify the circuit, while analysis passes only collect information about a circuit (to be used by other passes). Then, the ``run(dag)`` method is implemented, which does the main task. Finally, the pass is registered inside the ``qiskit.transpiler.passes`` module.\n",
    "\n",
    "This pass functions as follows: it traverses the DAG layer-by-layer (each layer is a group of operations that does not act on independent qubits, so in theory all operations in a layer can be done independently). For each operation, if it does not already meet the coupling map constraints, the pass identifies a swap path and inserts swaps to bring the two qubits close to each other.\n",
    "\n",
    "Follow the comments in the code for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.178919Z",
     "start_time": "2019-12-10T21:48:04.159510Z"
    }
   },
   "outputs": [],
   "source": [
    "from copy import copy\n",
    "\n",
    "from qiskit.transpiler.basepasses import TransformationPass\n",
    "from qiskit.transpiler import Layout\n",
    "from qiskit.circuit.library import SwapGate\n",
    "\n",
    "\n",
    "class BasicSwap(TransformationPass):\n",
    "    \"\"\"Maps (with minimum effort) a DAGCircuit onto a `coupling_map` adding swap gates.\"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 coupling_map,\n",
    "                 initial_layout=None):\n",
    "        \"\"\"Maps a DAGCircuit onto a `coupling_map` using swap gates.\n",
    "        \n",
    "        Args:\n",
    "            coupling_map (CouplingMap): Directed graph represented a coupling map.\n",
    "            initial_layout (Layout): initial layout of qubits in mapping\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.coupling_map = coupling_map\n",
    "        self.initial_layout = initial_layout\n",
    "\n",
    "    def run(self, dag):\n",
    "        \"\"\"Runs the BasicSwap pass on `dag`.\n",
    "        \n",
    "        Args:\n",
    "            dag (DAGCircuit): DAG to map.\n",
    "\n",
    "        Returns:\n",
    "            DAGCircuit: A mapped DAG.\n",
    "\n",
    "        Raises:\n",
    "            TranspilerError: if the coupling map or the layout are not\n",
    "            compatible with the DAG.\n",
    "        \"\"\"\n",
    "        new_dag = DAGCircuit()\n",
    "        for qreg in dag.qregs.values():\n",
    "            new_dag.add_qreg(qreg)\n",
    "        for creg in dag.cregs.values():\n",
    "            new_dag.add_creg(creg)\n",
    "        \n",
    "\n",
    "        if self.initial_layout is None:\n",
    "            if self.property_set[\"layout\"]:\n",
    "                self.initial_layout = self.property_set[\"layout\"]\n",
    "            else:\n",
    "                self.initial_layout = Layout.generate_trivial_layout(*dag.qregs.values())\n",
    "\n",
    "        if len(dag.qubits) != len(self.initial_layout):\n",
    "            raise TranspilerError('The layout does not match the amount of qubits in the DAG')\n",
    "\n",
    "        if len(self.coupling_map.physical_qubits) != len(self.initial_layout):\n",
    "            raise TranspilerError(\n",
    "                \"Mappers require to have the layout to be the same size as the coupling map\")\n",
    "            \n",
    "        canonical_register = dag.qregs['q']\n",
    "        trivial_layout = Layout.generate_trivial_layout(canonical_register)\n",
    "        current_layout = trivial_layout.copy()\n",
    "\n",
    "        for layer in dag.serial_layers():\n",
    "            subdag = layer['graph']\n",
    "\n",
    "            for gate in subdag.two_qubit_ops():\n",
    "                physical_q0 = current_layout[gate.qargs[0]]\n",
    "                physical_q1 = current_layout[gate.qargs[1]]\n",
    "                if self.coupling_map.distance(physical_q0, physical_q1) != 1:\n",
    "                    # Insert a new layer with the SWAP(s).\n",
    "                    swap_layer = DAGCircuit()\n",
    "                    swap_layer.add_qreg(canonical_register)\n",
    "\n",
    "                    path = self.coupling_map.shortest_undirected_path(physical_q0, physical_q1)\n",
    "                    for swap in range(len(path) - 2):\n",
    "                        connected_wire_1 = path[swap]\n",
    "                        connected_wire_2 = path[swap + 1]\n",
    "\n",
    "                        qubit_1 = current_layout[connected_wire_1]\n",
    "                        qubit_2 = current_layout[connected_wire_2]\n",
    "\n",
    "                        # create the swap operation\n",
    "                        swap_layer.apply_operation_back(SwapGate(),\n",
    "                                                        qargs=[qubit_1, qubit_2],\n",
    "                                                        cargs=[])\n",
    "\n",
    "                    # layer insertion\n",
    "                    order = current_layout.reorder_bits(new_dag.qubits)\n",
    "                    new_dag.compose(swap_layer, qubits=order)\n",
    "\n",
    "                    # update current_layout\n",
    "                    for swap in range(len(path) - 2):\n",
    "                        current_layout.swap(path[swap], path[swap + 1])\n",
    "\n",
    "            order = current_layout.reorder_bits(new_dag.qubits)\n",
    "            new_dag.compose(subdag, qubits=order)\n",
    "\n",
    "        return new_dag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test this pass on a small example circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.189596Z",
     "start_time": "2019-12-10T21:48:04.181850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f1f5e2747c0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = QuantumRegister(7, 'q')\n",
    "in_circ = QuantumCircuit(q)\n",
    "in_circ.h(q[0])\n",
    "in_circ.cx(q[0], q[4])\n",
    "in_circ.cx(q[2], q[3])\n",
    "in_circ.cx(q[6], q[1])\n",
    "in_circ.cx(q[5], q[0])\n",
    "in_circ.rz(0.1, q[2])\n",
    "in_circ.cx(q[5], q[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we construct a pass manager that contains our new pass. We pass the example circuit above to this pass manager, and obtain a new, transformed circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.207681Z",
     "start_time": "2019-12-10T21:48:04.191604Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-6348c3c81f5c>:50: DeprecationWarning: dag.qubits() and dag.clbits() are no longer methods. Use dag.qubits and dag.clbits properties instead.\n",
      "  if len(dag.qubits()) != len(self.initial_layout):\n"
     ]
    }
   ],
   "source": [
    "from qiskit.transpiler import PassManager\n",
    "from qiskit.transpiler import CouplingMap\n",
    "from qiskit import BasicAer\n",
    "pm = PassManager()\n",
    "coupling = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6]]\n",
    "coupling_map = CouplingMap(couplinglist=coupling)\n",
    "\n",
    "pm.append([BasicSwap(coupling_map)])\n",
    "\n",
    "out_circ = pm.run(in_circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.457320Z",
     "start_time": "2019-12-10T21:48:04.210267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.977x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.807143Z",
     "start_time": "2019-12-10T21:48:04.459740Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 923.377x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this pass only inserts the swaps necessary to make every two-qubit interaction conform to the device coupling map. It does not, for example, care about the direction of interactions, or the native gate set supported by the device. This is a design philosophy of Qiskit's transpiler: every pass performs a small, well-defined action, and the aggressive circuit optimization is achieved by the pass manager through combining multiple passes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transpiler Logging <a name='logging'></a>\n",
    "\n",
    "Due to the complexity of the internal operations that the transpiler is performing it's likely that you'll end up in a situation where you'd like to debug an issue or just understand more of what is happening inside the transpiler when you call it. To facilitate this the transpiler emits log messages as part of its normal operation. This logging uses the Python standard library `logging` module to emit the log messages. Python's standard logging was used because it allows Qiskit-Terra's logging to integrate in a standard way with other applications and libraries.\n",
    "\n",
    "For a more thorough introduction to Python logging refer to the [official documentation](https://docs.python.org/3/library/logging.html) and the tutorials and cookbook linked off of there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "    <b>Note:</b> Most of the <code>logging</code> module functions used in this section adjust global settings. If you run commands in this section it might effect the output from other cells if they are run in a different order.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring Python Standard Library Logging\n",
    "\n",
    "By default Python Standard Logging only prints log messages at the `WARNING`, `ERROR`, or `CRITICAL` log levels.\n",
    "Since none of the logs emitted by the transpiler use these log levels (they're all informative) you need to configure logging.\n",
    "\n",
    "The simplest way to do this is to just run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.813322Z",
     "start_time": "2019-12-10T21:48:04.809390Z"
    }
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "logging.basicConfig(level='DEBUG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `basicConfig()` function (see the docs here: https://docs.python.org/3/library/logging.html#logging.basicConfig) configures a root handler and formatter. We also specify the [log level](https://docs.python.org/3/library/logging.html#levels) to display with the `level` kwarg. Setting it to a level will also include and higher levels. For example, if you set it to `'INFO'` in addition to the `INFO` level this will also include the `WARNING`, `ERROR`, and `CRITICAL` log levels.\n",
    "\n",
    "Now the python environment in this notebook is configured to emit log messages to stderr when you run the transpiler. For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "    <b>Note:</b> <code>basicConfig()</code> will only work when called the first time it's called. It detects if a root handler and formatter have already been setup (either by using an earlier <code>basicConfig()</code> call or otherwise) and does nothing if they have. Further adjustments will have to by interacting with the handler directly.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.870900Z",
     "start_time": "2019-12-10T21:48:04.815673Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:qiskit.transpiler.runningpassmanager:Pass: SetLayout - 0.00477 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: TrivialLayout - 0.04864 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Layout2qDistance - 0.45657 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FullAncillaAllocation - 0.08535 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: EnlargeWithAncilla - 0.08416 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: ApplyLayout - 0.64754 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Unroll3qOrMore - 0.05555 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckMap - 0.05698 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: UnrollCustomDefinitions - 0.08774 (ms)\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Begining basis search from frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}) to frozenset({'reset', 'cx', 'u3', 'barrier', 'u1', 'measure', 'id', 'u2', 'snapshot'}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('h', 1), ('cx', 2), ('u3', 1)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Transformation path:\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:x/1 => []\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:h/1 => []\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation path search completed in 0.010s.\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: x/1 [] =>\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('x', 1) x, [] from \n",
      "        ┌───┐\n",
      "q293_0: ┤ x ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('x', 1) x, [] to\n",
      "        ┌─────────────┐\n",
      "q293_0: ┤ U3(pi,0,pi) ├\n",
      "        └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: h/1 [] =>\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('h', 1) h, [] from \n",
      "        ┌───┐\n",
      "q294_0: ┤ h ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('h', 1) h, [] to\n",
      "        ┌──────────┐\n",
      "q294_0: ┤ U2(0,pi) ├\n",
      "        └──────────┘\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation paths composed in 0.020s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation instructions replaced in 0.000s.\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: BasisTranslator - 36.93509 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckCXDirection - 0.09489 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXDirection - 0.78416 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: RemoveResetInZeroState - 0.03719 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.05436 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.03481 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 1.50800 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.08798 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.03672 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01812 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.23603 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.06080 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.02980 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01717 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.23818 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.07939 (ms)\n",
      "INFO:qiskit.compiler.transpile:Total Transpile Time - 75.43111 (ms)\n"
     ]
    }
   ],
   "source": [
    "from qiskit.test.mock import FakeTenerife\n",
    "\n",
    "\n",
    "log_circ = QuantumCircuit(2, 2)\n",
    "log_circ.h(0)\n",
    "log_circ.h(1)\n",
    "log_circ.h(1)\n",
    "log_circ.x(1)\n",
    "log_circ.cx(0, 1)\n",
    "log_circ.measure([0,1], [0,1])\n",
    "\n",
    "backend = FakeTenerife()\n",
    "\n",
    "transpile(log_circ, backend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can clearly see here when calling `transpile()` it now prints 2 types of log messages. The first is at the `INFO` log level and come from the pass manager. These indicate each pass that was executed and how long that took. The second are at the `DEBUG` level and come from the StochasticSwap pass and describes the internal operation of that pass. It's useful for debugging issues in the pass's operation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting the log level for the transpiler\n",
    "\n",
    "The qiskit transpiler uses a single namespace ``qiskit.transpiler``, as used by ``logging.getLogger('qiskit.transpiler')``. This makes it very easy to adjust the log level for just the transpiler. For example if you only wish to see log messages at the INFO level or above you can run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:04.917836Z",
     "start_time": "2019-12-10T21:48:04.872823Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:qiskit.transpiler.runningpassmanager:Pass: SetLayout - 0.00572 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: TrivialLayout - 0.04363 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Layout2qDistance - 0.42200 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FullAncillaAllocation - 0.06890 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: EnlargeWithAncilla - 0.08631 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: ApplyLayout - 0.56601 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Unroll3qOrMore - 0.02265 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckMap - 0.04959 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: UnrollCustomDefinitions - 0.04530 (ms)\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation path search completed in 0.001s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation paths composed in 0.001s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation instructions replaced in 0.000s.\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: BasisTranslator - 4.98986 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckCXDirection - 0.07892 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXDirection - 0.71764 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: RemoveResetInZeroState - 0.02217 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.04625 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01764 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 2.09403 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.07653 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.02766 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01836 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.25296 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.06986 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.02646 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01407 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.23460 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.05984 (ms)\n",
      "INFO:qiskit.compiler.transpile:Total Transpile Time - 38.27548 (ms)\n"
     ]
    }
   ],
   "source": [
    "logging.getLogger('qiskit.transpiler').setLevel('INFO')\n",
    "transpile(log_circ, backend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up logging to deal with parallel execution\n",
    "\n",
    "When running the transpiler with multiple circuits by default these circuits are transpiled in parallel. If you want to do this with logging enabled and be able to understand the output some additional steps are required.\n",
    "\n",
    "If you were just to enable logging as above and then pass `transpile()` multiple circuits you'll get results that are difficult to decipher. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:05.069815Z",
     "start_time": "2019-12-10T21:48:04.920183Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:qiskit.transpiler.runningpassmanager:Pass: SetLayout - 0.01788 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: TrivialLayout - 0.14687 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: SetLayout - 0.01669 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Layout2qDistance - 0.79012 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: TrivialLayout - 0.17047 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Layout2qDistance - 0.97585 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FullAncillaAllocation - 0.22602 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: EnlargeWithAncilla - 0.09418 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FullAncillaAllocation - 0.25630 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: EnlargeWithAncilla - 0.10729 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: ApplyLayout - 0.69308 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: ApplyLayout - 0.62561 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Unroll3qOrMore - 0.02956 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Unroll3qOrMore - 0.02003 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckMap - 0.05436 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckMap - 0.04601 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: UnrollCustomDefinitions - 0.19526 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: UnrollCustomDefinitions - 0.13828 (ms)\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Begining basis search from frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}) to frozenset({'reset', 'cx', 'u3', 'barrier', 'u1', 'measure', 'id', 'u2', 'snapshot'}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Begining basis search from frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}) to frozenset({'reset', 'cx', 'u3', 'barrier', 'u1', 'measure', 'id', 'u2', 'snapshot'}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('h', 1), ('cx', 2), ('u3', 1)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Transformation path:\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Inspecting basis frozenset({('measure', 1), ('h', 1), ('cx', 2), ('u3', 1)}).\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Transformation path:\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:x/1 => []\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:x/1 => []\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:h/1 => []\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:h/1 => []\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation path search completed in 0.015s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation path search completed in 0.019s.\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: x/1 [] =>\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: x/1 [] =>\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('x', 1) x, [] from \n",
      "        ┌───┐\n",
      "q303_0: ┤ x ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('x', 1) x, [] to\n",
      "        ┌─────────────┐\n",
      "q303_0: ┤ U3(pi,0,pi) ├\n",
      "        └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: h/1 [] =>\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('x', 1) x, [] from \n",
      "        ┌───┐\n",
      "q303_0: ┤ x ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('h', 1) h, [] from \n",
      "        ┌───┐\n",
      "q304_0: ┤ h ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('x', 1) x, [] to\n",
      "        ┌─────────────┐\n",
      "q303_0: ┤ U3(pi,0,pi) ├\n",
      "        └─────────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('h', 1) h, [] to\n",
      "        ┌──────────┐\n",
      "q304_0: ┤ U2(0,pi) ├\n",
      "        └──────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Composing transform step: h/1 [] =>\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updating transform for mapped instr ('h', 1) h, [] from \n",
      "        ┌───┐\n",
      "q304_0: ┤ h ├\n",
      "        └───┘\n",
      "DEBUG:qiskit.transpiler.passes.basis.basis_translator:Updated transform for mapped instr ('h', 1) h, [] to\n",
      "        ┌──────────┐\n",
      "q304_0: ┤ U2(0,pi) ├\n",
      "        └──────────┘\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation paths composed in 0.025s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation instructions replaced in 0.000s.\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation paths composed in 0.026s.\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: BasisTranslator - 49.03197 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckCXDirection - 0.13423 (ms)\n",
      "INFO:qiskit.transpiler.passes.basis.basis_translator:Basis translation instructions replaced in 0.000s.\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: BasisTranslator - 54.11601 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXDirection - 0.91052 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: RemoveResetInZeroState - 0.04077 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CheckCXDirection - 0.15759 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXDirection - 0.79799 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.11158 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.04745 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: RemoveResetInZeroState - 0.04077 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 1.99819 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.08464 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.04339 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.10943 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.03314 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.03242 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.21958 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.07629 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.02503 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 2.43950 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01597 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.12016 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.17095 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.05364 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.03505 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01788 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.18024 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.07749 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Depth - 0.03338 (ms)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:qiskit.transpiler.runningpassmanager:Pass: FixedPoint - 0.01717 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: Optimize1qGates - 0.15736 (ms)\n",
      "INFO:qiskit.transpiler.runningpassmanager:Pass: CXCancellation - 0.07129 (ms)\n",
      "INFO:qiskit.compiler.transpile:Total Transpile Time - 195.16397 (ms)\n"
     ]
    }
   ],
   "source": [
    "# Change log level back to DEBUG\n",
    "logging.getLogger('qiskit.transpiler').setLevel('DEBUG')\n",
    "# Transpile multiple circuits\n",
    "circuits = [log_circ, log_circ]\n",
    "transpile(circuits, backend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see here we get log messages from all 3 circuits being transpiled together. There is no way to know which pass is part of which circuit's transpilation.  Luckily Python logging provides tools to deal with this. The simplest one is to just change the [log formatter](https://docs.python.org/3/library/logging.html#logging.Formatter) so that includes additional information so we can associate a log message with the process it came from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:05.077804Z",
     "start_time": "2019-12-10T21:48:05.073526Z"
    }
   },
   "outputs": [],
   "source": [
    "formatter = logging.Formatter('%(name)s - %(processName)-10s - %(levelname)s: %(message)s')\n",
    "handler = logging.getLogger().handlers[0]\n",
    "handler.setFormatter(formatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then rerun the `transpile()` call and see the new log formatter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:05.205597Z",
     "start_time": "2019-12-10T21:48:05.081153Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: SetLayout - 0.01597 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: TrivialLayout - 0.17691 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: SetLayout - 0.01693 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Layout2qDistance - 1.08218 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: TrivialLayout - 0.11516 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Layout2qDistance - 0.69022 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: FullAncillaAllocation - 0.16642 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: EnlargeWithAncilla - 0.09775 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: FullAncillaAllocation - 0.31352 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: EnlargeWithAncilla - 0.15903 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: ApplyLayout - 0.44227 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Unroll3qOrMore - 0.02933 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CheckMap - 0.05007 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: ApplyLayout - 0.67401 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: UnrollCustomDefinitions - 0.12970 (ms)\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - INFO: Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Unroll3qOrMore - 0.02599 (ms)\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Begining basis search from frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}) to frozenset({'reset', 'cx', 'u3', 'barrier', 'u1', 'measure', 'id', 'u2', 'snapshot'}).\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CheckMap - 0.04196 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: UnrollCustomDefinitions - 0.20337 (ms)\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - INFO: Begin BasisTranslator from source basis {('measure', 1), ('x', 1), ('h', 1), ('cx', 2)} to target basis {'cx', 'barrier', 'id', 'reset', 'u3', 'u1', 'measure', 'u2', 'snapshot'}.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Inspecting basis frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}).\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Begining basis search from frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}) to frozenset({'reset', 'cx', 'u3', 'barrier', 'u1', 'measure', 'id', 'u2', 'snapshot'}).\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Inspecting basis frozenset({('measure', 1), ('h', 1), ('cx', 2), ('u3', 1)}).\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Inspecting basis frozenset({('measure', 1), ('x', 1), ('h', 1), ('cx', 2)}).\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Inspecting basis frozenset({('measure', 1), ('h', 1), ('cx', 2), ('u3', 1)}).\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Transformation path:\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Transformation path:\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: x/1 => []\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: x/1 => []\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: h/1 => []\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: h/1 => []\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - INFO: Basis translation path search completed in 0.018s.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - INFO: Basis translation path search completed in 0.014s.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Composing transform step: x/1 [] =>\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Composing transform step: x/1 [] =>\n",
      "     ┌─────────────┐\n",
      "q_0: ┤ U3(pi,0,pi) ├\n",
      "     └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Updating transform for mapped instr ('x', 1) x, [] from \n",
      "        ┌───┐\n",
      "q303_0: ┤ x ├\n",
      "        └───┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Updating transform for mapped instr ('x', 1) x, [] from \n",
      "        ┌───┐\n",
      "q303_0: ┤ x ├\n",
      "        └───┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Updated transform for mapped instr ('x', 1) x, [] to\n",
      "        ┌─────────────┐\n",
      "q303_0: ┤ U3(pi,0,pi) ├\n",
      "        └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Updated transform for mapped instr ('x', 1) x, [] to\n",
      "        ┌─────────────┐\n",
      "q303_0: ┤ U3(pi,0,pi) ├\n",
      "        └─────────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Composing transform step: h/1 [] =>\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Composing transform step: h/1 [] =>\n",
      "     ┌──────────┐\n",
      "q_0: ┤ U2(0,pi) ├\n",
      "     └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Updating transform for mapped instr ('h', 1) h, [] from \n",
      "        ┌───┐\n",
      "q304_0: ┤ h ├\n",
      "        └───┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Updating transform for mapped instr ('h', 1) h, [] from \n",
      "        ┌───┐\n",
      "q304_0: ┤ h ├\n",
      "        └───┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - DEBUG: Updated transform for mapped instr ('h', 1) h, [] to\n",
      "        ┌──────────┐\n",
      "q304_0: ┤ U2(0,pi) ├\n",
      "        └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - DEBUG: Updated transform for mapped instr ('h', 1) h, [] to\n",
      "        ┌──────────┐\n",
      "q304_0: ┤ U2(0,pi) ├\n",
      "        └──────────┘\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - INFO: Basis translation paths composed in 0.020s.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - INFO: Basis translation paths composed in 0.020s.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-5 - INFO: Basis translation instructions replaced in 0.000s.\n",
      "qiskit.transpiler.passes.basis.basis_translator - ForkProcess-6 - INFO: Basis translation instructions replaced in 0.000s.\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: BasisTranslator - 40.80915 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: BasisTranslator - 45.52960 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CheckCXDirection - 0.11969 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CheckCXDirection - 0.13542 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CXDirection - 0.81277 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CXDirection - 0.93150 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: RemoveResetInZeroState - 0.03934 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: RemoveResetInZeroState - 0.04005 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Depth - 0.12755 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Depth - 0.09656 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: FixedPoint - 0.03791 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: FixedPoint - 0.04053 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Optimize1qGates - 2.46310 (ms)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CXCancellation - 0.13208 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Depth - 0.03552 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: FixedPoint - 0.02217 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Optimize1qGates - 2.03133 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CXCancellation - 0.14567 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Optimize1qGates - 0.18024 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CXCancellation - 0.07057 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Depth - 0.04125 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: FixedPoint - 0.02265 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Depth - 0.03672 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: Optimize1qGates - 0.31304 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: FixedPoint - 0.01884 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-6 - INFO: Pass: CXCancellation - 0.06986 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Optimize1qGates - 0.17571 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CXCancellation - 0.19479 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Depth - 0.04315 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: FixedPoint - 0.02480 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: Optimize1qGates - 0.43654 (ms)\n",
      "qiskit.transpiler.runningpassmanager - ForkProcess-5 - INFO: Pass: CXCancellation - 0.05555 (ms)\n",
      "qiskit.compiler.transpile - MainProcess - INFO: Total Transpile Time - 172.73402 (ms)\n"
     ]
    }
   ],
   "source": [
    "transpile(circuits, backend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the format for the log messages has been changed and it includes a process name for each of the transpilation processes so it's at least clear which log messages go together.\n",
    "\n",
    "There are many different options for how you can configure, this example is pretty limited. Refer to the documentation for more examples and options to build more sophisticated use cases that suit your specific use case or preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:10.920429Z",
     "start_time": "2019-12-10T21:48:10.912305Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.20.0</td></tr><tr><td>Terra</td><td>0.15.1</td></tr><tr><td>Aer</td><td>0.6.1</td></tr><tr><td>Ignis</td><td>0.4.0</td></tr><tr><td>Aqua</td><td>0.7.5</td></tr><tr><td>IBM Q Provider</td><td>0.8.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.8.5 | packaged by conda-forge | (default, Jul 31 2020, 02:39:48) \n",
       "[GCC 7.5.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>30.82091522216797</td></tr><tr><td colspan='2'>Tue Sep 08 13:39:41 2020 EDT</td></tr></table>"
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       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2020.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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